Bibliographic record
Abstract
2 agriculture see also pastoralism capability inequalities 150-51 crop switching 129-30 fisheries 127-8 forestry 128 sedentary agriculture 127 societal adaptation strategies 155 specialized export-oriented agriculture 129-31 Aitken, D. 346 Aklilu, Y. 320 Alaska, climate change impacts context, importance of 296-9 human security in adaptive strategies 301 Inuit people 283-4, 287, 289-99, 301 land settlement agreements 301 migratory animals and birds 290-92, 295-6 permafrost warming 294-5 water resources 287-8 Albania human security indicators 265-71 Algeria human security indicators 265-71 Alkire, S. 264 animals and birds in Arctic, climate change impact on 290-92, 295-6 Annan, Kofi 29, 238 Antarctica environmental management framework 284 environmental patterns 283 global climate, biosystems role in 282-3 human security 283-4 Arab Spring 256-7, 259, 261 Arctic, climate change impacts adaptation strategies, influences on 299-301 biosystems role in 282-3 context, importance of 296-9 economic benefits of climate change 288-90 environmental patterns 283, 285 extractive industries 288-9, 297 human security 126-7, 283, 287, 299-301 Inuit/ indigenous peoples 283-4, 287, 289-99 permafrost warming 285, 294-6 political impacts 288-9 sea ice melt 283, 285-7, 295 temperature increases 283, 285, 287 trends and predictions 283, 285-6 water resources 287-8 wildlife, climate change impacts 286-7, 290-92, 295-6 Argentina disaster management plans 174-5 Arrhenius, Svante 94 Ash, Timothy G. 200 Ashton, Peter 80 Australia Green Home programme 365-6
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.781 | 0.628 |
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".