Bibliographic record
Abstract
abatement costs 107 abatement effects emission taxes 4-5 heterogeneity in 18 abatement technologies 129-32, 125-7, 129-30, 143-4, 160 ABC policy, Netherlands 241-2 abstraction impacts 52 Active 46 aggregation procedures 58-60, 64-5 agricultural economic studies 47 Agricultural Nonpoint Source Pollution (AGNPS) model 62 air pollution, transport 217-20 air quality patterns 53-4 air travel 212, 214, 223, 224-5, 244 airport noise 216-17, 234 Alberta forested habitats 56 non-designated camping areas 77, 78 allocation studies 62 Amazon, deforestation 260 ambient pollution 21 Anhui province, China 102 annual income 13, 14-15, 16, 19 Anselin, Luc 77 anti-trust 197 Arc/Info 44, 49 ArcView 44, 49 Argentina, pollution control 103 Asia, pollution control 95, 99-100, 104, 111-12 auctioned emission permits 15-16, 28, 31, 32, 132, 185, 187-8 Australia, pollution control 95-6, 99 Australian Household Expenditure Surveys 13-14 benchmark standards, pollution 100, 106, 107 benefit function transfer, GIS 65-73 Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.736 | 0.540 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".