Bibliographic record
Abstract
List of figures 1.1 Burden of a product tax 3 1.2 Abatement under an emissions tax 4 2.1 Distribution of household income levels in the counties of the Canadian province of Ontario 45 2.2 Representing real-world features as vector or raster data 48 2.3 GDP generated in flood-prone areas of the Netherlands 50 2.4 GIS viewshed calculation of viewable area from one point on a specified property 52 2.5 Modelled emissions of CO and NO 2 in Birmingham, UK; cumulative frequency of persons in five ethnic groups with respect to exposure to CO pollution 55 2.6 Conventional approach to aggregating WTP data; GIS-based approach to aggregation allowing for spatial variation in population and income distribution and distance decay and income influences upon marginal WTP 59 2.7 Isochrone map for a single recreational site; isochrone map for all wildlife parks in the UK 63 2.8 Examples of types of neighbourhood analysis for raster data 66 2.9 Official counts of recreational visits to UK woodlands and visits predicted by GIS-based models 69 2.10 GIS-generated map of the marginal value of predicted woodland recreation demand for potential forest sites in Wales 72 2.11 A local indicators of spatial association map for expert ratings of the intensity of random camping in the East Slopes Region of Alberta, Canada 78 2.12 Two illustrations of first-order contiguity binary weights matrices constructed from a 3 ϫ 3 regular lattice 82 2.13 Spatial distribution of fishing package prices at remote tourism sites in Ontario 84 2.14 Spatial autocorrelograms of the natural logarithm of weekly prices at fly-in accessible tourism sites in Ontario 85 5.1 The chain from transport activity to valuation of disturbance 210 5.2 Competition between transport modes in urban areas 242 vi
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.833 | 0.743 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".