Bibliographic record
Abstract
blue water exports of 235 green water exports of 237 Nova Scotia 104 oil and gas industry of 210-12, 214 tar sands 211-13 Toronto 412 virtual water exports of 234 capital 135, 140 financial 140 health 140 human 136, 139, 375 investment 198, 375-6 social 132, 135-6, 141, 349 stocks 8 carbon capture and sequestration (CCS) 221 carbon dioxide (CO 2 ) 415, 418, 420-21 direct 416-18 use of OLS regression in prediction of 419 emissions 231, 243, 285, 305, 309, 408-9, 412, 420 reduction of 376 urban metabolism paradigm 408, 411-15 indirect 416, 418 urban-rural contrast 417-18, 422 Carnegie Mellon University 207 faculty of 316 Chesapeake Bay Foundation 89 Chesapeake Bay Program Living Resources Committee 89 Scientific and Technical Advisory Committee (STAC) 89, 95 China, People's Republic of 210, 243 agricultural imports of 233 blue water imports of 236, 240 net 235 economy of 234 green water exports of 237 Jiangshu Province 225 National IO accounts 315 oil field of 213 Shanxi Province 8, 300 virtual water exports of 233-4 grey 238-40 water availability of 234 yixing Economic and Technological Development Area (yETDA) 287 Chiquita Banana Game, The 484, 486 chlorofluorocarbons (CFCs) 14 civil society organizations (CSOs) 103, 361 climate change 81-2, 94-5, 98-9, 408-9 cultural model of 96-7 impact assessment 163 organization (CCO) 190 policies 99 political knowledge of 20
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.694 | 0.447 |
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".