Confidence assessment in the teaching of basic science

A scheme is described for including information about confidence in the computer‐based assessment of students. After each answer, students declare a confidence level of 1, 2, or 3. If the answer is correct, then this is the mark awarded. If not, marks of 0, ‐2, or ‐6 are awarded Students do well on this scheme if they can discriminate between when they are sure of correct answers and when they are partly guessing. In self‐assessment, students are trained to reflect on their reasoning, and to develop the skills of correct confidence judgement. The task of writing tests is simplified, since it becomes less important to ask complex questions. Simple direct questions discriminate better between students than they do with ordinary marking. Good students answer correctly with high confidence, while weak students moderate their confidence level if they know they are uncertain, or else lose heavily when they make mistakes. Preliminary data are presented from self‐assessment trials amongst medical students

Mathematical model predictions and optimization study of the gamma ray atmospheric density sensor

Mathematical model predictions and optimization study of gamma ray atmospheric density senso

U.S. Law of the Sea Cruise to Map the Southern Flank of the Kingman Reef-Palmyra Atoll section of the Line Islands, Equatorial Pacific Ocean

U.S. Law of the Sea Cruise to Map the Southern Flank of the Kingman Reef-Palmyra Atoll section of the Line Islands, Equatorial Pacific Ocean CRUISE KM1009 May 17, to June 16, 2010 Pago Pago, American Samoa to Honolulu, H

U.S. Law of the Sea Cruise to Complete the Mapping of Necker Ridge, Central Pacific Ocean

U.S. Law of the Sea Cruise to Complete the Mapping of Necker Ridge, Central Pacific Ocean CRUISE KM1121 July 31, to August 10, 2011 Honolulu, HI to Honolulu, H

Experimental verification of gamma ray atmospheric density sensor mathematical model predictions

Experimental verification of mathematical model performance predictions for gamma ray atmospheric density sensor

U.S. Law of the Sea Cruise to Map the Foot of the Slope of the Northeast U.S. Atlantic Continental Margin: Leg 6

U.S. Law of the Sea Cruise to Map the Foot of the Slope of the Northeast U.S. Atlantic Continental Margin: Leg 6 Cruise KNOX17RR May 1 – 31, 2008 Ft. Lauderdale, FL to Woods Hole, M

Site Description for the University of Nebraska's Sandhills Agricultural Laboratory

The Sandhills Agricultural Laboratory is operated by the University of Nebraska. The laboratory is located in the south-central part of the Nebraska Sandhills near Tryon, Nebraska (41 deg. 37' N; 100 deg. 50' W). The laboratory is surrounded on the west and south by native rangeland vegetation, on the south by a large field of corn irrigated by a center pivot, and on the east by wheat stubble. This site is appropriate for moisture stress studies since rainfall is almost always inadequate to meet evaporative demands of agricultural crops during most of the growing season and the sandy soils (Valentine fine sand) at the site do not store large quantities of water. Various levels of water stress are achieved through irrigation from solid set sprinklers

Comparative review of the effects of organic farming on biodiversity (OF0149)

This is the final report of Defra project OF0149 1. The report reviews the impact of different farming regimes and makes a comparative study of their influence on the biodiversity of arable farmland. 2. Within this review, the evaluation of impacts on biodiversity focuses on species and habitats, and includes both the number, abundance and activity of species (section 1.3). 3. Five farming regimes are defined and discussed, namely Conventional Arable, Conventional Mixed Lowland, Organic and two integrated production regimes - LEAF (Linking Environment and Farming) and IFS-Experimental regimes The main differences between the regimes in relation to the use of external inputs and other agricultural practices are discussed. The review draws on both UK and European information (section 1.4). 4. The effect of each farming regime on biodiversity is assessed according to the agricultural practices adopted and to the occurrence and management of uncropped land present. Agricultural practices are reviewed within the following categories: cultivation, crop production, crop protection and post-cropping practice (section 2.1). 5. Among the agricultural practices examined, those associated with crop protection and the artificial inputs associated with crop production were seen as the most adverse for biodiversity. Several practices were seen to benefit the biodiversity of arable land. These included set-aside, crop rotations with grass leys, spring sowing, permanent pasture, green manuring and intercropping (section 2.7). 6. Uncropped areas, such as sown grass strips (beetle banks), grass margins and conservation headlands, were seen as critical for the maintenance of biodiversity on arable farmland. Changes in the balance of cropped to uncropped land within some farming regimes, linked to increase in field size, have had a major impact on the diversity of flora and fauna associated with those regimes (section 3.4). 7. Based on the evaluation of agricultural practices used, the occurrence of uncropped land and the extent of the farming regime within England and Wales, it was concluded that Conventional Arable regimes act effectively to maintain the impoverished status of biodiversity on arable land. Extreme examples can be found of intensively managed farms that further deplete biodiversity and sympathetically managed farms that try to enhance it. Increased adoption of agricultural practices such as direct drilling, use of farmyard manure, set-aside, use of crop rotations with leys, or an increase in the incidence and sympathetic management of uncropped areas may well assist biodiversity on farms within this regime (sections 4.3 & 4.4) 8. Organic regimes were shown to have an overall benefit for biodiversity at the farm level, both in terms of the agricultural practices adopted and in the occurrence and management of uncropped areas (sections 4.3 & 4.4). 9. Conventional Mixed Lowland and LEAF regimes were both seen to have the potential for enhancing biodiversity on arable land. Here, adverse impacts associated with crop protection and crop production may be mitigated by beneficial effects associated with post-cropping practices, the occurrence of permanent pasture and uncropped land. At present, the extent to which enhancement may be achieved, may well depend on the extent, condition and management of uncropped land present within these regimes (sections 4.3 & 4.4). 10. IFS-experimental regimes were seen to have a beneficial effect on biodiversity, due to the stringent procedures used for targeting herbicides and pesticides and for establishing and managing uncropped areas. At present these regimes occupy a tiny area of the national resource of arable land and thus their impact on national biodiversity is likely to be insignificant at the present time (sections 4.3 & 4.4). 11. A number of areas are highlighted for further consideration. These include: • monitoring of biodiversity on farms pre- and post- conversion to organic farming, • comparative studies that focus on the effectiveness of different regimes or agricultural practices in enhancing biodiversity on species-impoverished intensively managed arable land, • manipulative experiments to determine the optimal balance of cropped to uncropped areas for enhancing biodiversity, • manipulative experiments to examine the separate impacts of rotational regimes and agricultural inputs on biodiversity, • an economic assessment of the costs and benefits in both production and biodiversity terms, of conversion to organic, integrated production or uptake of available agri-environment schemes

Capturing UK real estate volitility

Volatility, or the variability of the underlying asset, is one of the key fundamental components of property derivative pricing and in the application of real option models in development analysis. There has been relatively little work on volatility in real terms of its application to property derivatives and the real options analysis. Most research on volatility stems from investment performance (Nathakumaran & Newell (1995), Brown & Matysiak 2000, Booth & Matysiak 2001). Historic standard deviation is often used as a proxy for volatility and there has been a reliance on indices, which are subject to valuation smoothing effects. Transaction prices are considered to be more volatile than the traditional standard deviations of appraisal based indices. This could lead, arguably, to inefficiencies and mis-pricing, particularly if it is also accepted that changes evolve randomly over time and where future volatility and not an ex-post measure is the key (Sing 1998). If history does not repeat, or provides an unreliable measure, then estimating model based (implied) volatility is an alternative approach (Patel & Sing 2000). This paper is the first of two that employ alternative approaches to calculating and capturing volatility in UK real estate for the purposes of applying the measure to derivative pricing and real option models. It draws on a uniquely constructed IPD/Gerald Eve transactions database, containing over 21,000 properties over the period 1983-2005. In this first paper the magnitude of historic amplification associated with asset returns by sector and geographic spread is looked at. In the subsequent paper the focus will be upon model based (implied) volatility