A Bark Thickness Model for White Spruce in Alaska Northern Forests

Here we developed a simple linear model to estimate white spruce bark thickness in the northern forests of Alaska. Data were collected from six areas throughout interior and southcentral Alaska. Geographic variation of bark thickness was tested between the Alaska statewide model and for each geographic area. The results show that the Alaska statewide model is accurate, simple, and robust, and has no practical geographic variation over the six areas. The model provides accurate estimates of the bark thickness for white spruce trees in Alaska for a wide array of future studies, and it is in demand by landowners and forest managers to support their management decisions.We are obligated to Carol E. Lewis and Edmond C. Packee for supporting this bark thickness research. This research was also supported in part by the United States Department of Agriculture, McIntire-Stennis Act Fund ALK-03-12, and by the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks.We thank the associate editor, Han Chen, and an anonymous reviewer for their helpful comments

MP 2012-01

In 1994 the University of Alaska Fairbanks, School of Natural Resources and Agricultural Sciences, Agricultural and Forestry Experiment Station began a project to establish permanent sample plots (PSP) throughout the forests of northern and southcentral Alaska. Objectives of the project are to establish and maintain a system of PSPs to monitor forest growth, yield, forest health, and ecological conditions/change (Malone et al., 2009). To date, 603 PSPs have been established on 201 sites throughout interior and southcentral Alaska. The PSPs are square and 0.1 acre in size and in clusters of three. PSPs are remeasured at a five-year interval. The number of plot remeasurements after establishment ranges from one to three times. A large amount of data is collected at each site at time of establishment and at subsequent remeasurements. Four databases contain all the data: tree measurement and characteristics, site description, regeneration, and vegetation data. Vegetation data collected on the 0.1 acre PSPs includes species (trees shrub, herb, grass, and non-vascular plants) and cover, an estimate of the amount of the plot covered by the crown of each species (cover class) (Daubenmire, 1959). The vegetation database can be used by land managers and researchers to study species diversity and forest succession in addition to long-term monitoring of forest health. The species listed in Appendix 1 and in the vegetation database are presented by categories: tree, shrub, herb, grass, rush, sedge, fern, club moss, lichen, moss, and liverwort

Circular 53

The regeneration of interior Alaska’s commercial forest lands is mandated by Alaska’s Forest Resources and Practices Act (1979). This act requires that regeneration be established adequate to ensure a sustained yield on forested lands from which the timber has been harvested. Post-logging regeneration efforts now are aimed at exposing mineral soil for the natural seeding of white spruce. Soil exposure has been accomplished by blade scarifying with a crawler tractor which provides large seed sites or by using a Bracke-type patch scarifier which produces small seed sites of about 2 ft2. Arlidge (1967) reports that larger seedbeds have greater regeneration success than smaller ones. Some researchers have found that the regeneration of the larger plots may be too successful, requiring weeding and precommercial thinning to bring stocking to satisfactory levels (Zasada and Grigal 1978). The Alaska Division of Forestry (DOF) has not been satisfied with the cost or effectiveness of either of these site-preparation practices.Introduction -- Methods -- Results and Discussion: Contractor #2, Contractor #3 -- Comparisons -- Conclusions -- Resources Cite

Total and Merchantable Volume of White Spruce in Alaska

White spruce (Picea glauca [Moench] Voss) is a valuable commercial species found in interior and southcentral Alaska. Numerous regional and local volume tables or equations exist; however, no statewide model exists or has been tested for accuracy. There is a demand for an accurate model to determine the cubic-foot volume of white spruce trees in Alaska. Multiple models were developed for white spruce to estimate total and merchantable cubic-foot volume to a 2-, 4-, and 6-in. top. These multiple-entry (diameter and height) models were developed for both inside and outside bark volume from a 6-in. stump. The models were tested on a regional basis at various geographic locations and were shown to be highly accurate. The Alaska models chosen have R2 at or near 0.99 and mean square error from 0 to 0.16 for all models. These models are shown to be superior to other white spruce models in Alaska.This research was supported in part by the US Department of Agriculture, McIntire-Stennis Act Fund ALK-03-12, and by the School of Natural Resources and Agricultural Sciences, University of Alaska Fairbanks

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance

Internal Markets for Supply Chain Capacity Allocation

This paper explores the possibility of solving supply chain capacity allocation problems using internal markets among employees of the same company. Unlike earlier forms of transfer pricing, IT now makes it easier for such markets to involve many employees, finegrained transactions, and frequently varying prices. The paper develops a formal model of such markets, proves their optimality in a baseline condition, and then analyzes various potential market problems and solutions. Interestingly, these proposed solutions are not possible in a conventional market because they rely on the firm's ability to pay market participants based on factors other than just the profitability of their market transactions. For example, internal monopolies can be ameliorated by paying internal monopolists on the basis of corporate, not individual, profits. Incentives for collusion among peers can be reduced by paying participants based on their profits relative to peers. Profit-reducing competition among different sales channels can be reduced by imposing an internal sales tax. And problems caused by fixed costs can be avoided by combining conditional internal markets with a pivot mechanism

Integrated information as a metric for group interaction

Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration

Integrated information as a metric for group interaction

Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration

Do Some Business Models Perform Better than Others?

This paper defines four basic business models based on what asset rights are sold (Creators, Distributors, Landlords and Brokers) and four variations of each based on what type of assets are involved (Financial, Physical, Intangible, and Human). Using this framework, we classified the business models of all 10,970 publicly traded firms in the US economy from 1998 through 2002. Some of these classifications were done manually, based on the firms' descriptions of sources of revenue in their financial reports; the rest were done automatically by a rule-based system using the same data. Based on this analysis, we first document important stylized facts about the distribution of business models in the U.S. economy. Then we analyze the firms' financial performance in three categories: market value, profitability, and operating efficiency. We find that no model outperforms others on all dimensions. Surprisingly, however, we find that some models do, indeed, have better financial performance than others. For instance, Physical Creators (which we call Manufacturers) and Physical Landlords have greater cash flow on assets, and Intellectual Landlords have poorer q's, than Physical Distributors (Wholesaler/Retailers). These findings are robust to a large number of robustness checks and alternative interpretations. We conclude with some hypotheses to explain our findings.business models; performance

Harnessing Crowds: Mapping the Genome of Collective Intelligence

Over the past decade, the rise of the Internet has enabled the emergence of surprising new forms of collective intelligence. Examples include Google, Wikipedia, Threadless, and many others. To take advantage of the possibilities these new systems represent, it is necessary to go beyond just seeing them as a fuzzy collection of “cool” ideas. What is needed is a deeper understanding of how these systems work. This article offers a new framework to help provide that understanding. It identifies the underlying building blocks—to use a biological metaphor, the “genes”—at the heart of collective intelligence systems. These genes are defined by the answers to two pairs of key questions: – Who is performing the task? Why are they doing it? – What is being accomplished? How is it being done? The paper goes on to list the genes of collective intelligence—the possible answers to these key questions—and shows how combinations of genes comprise a “genome” that characterizes each collective intelligence system. In addition, the paper describes the conditions under which each gene is useful and the possibilities for combining and re-combining these genes to harness crowds effectively. Using this framework, managers can systematically consider many possible combinations of genes as they seek to develop new collective intelligence systems. ∗ University of Maryland