Bibliographic record
Abstract
pollution, 93, 94 Air quality, 93, 94 Albedo, 73, 175, 183,[234][235][236][237][238][239][240][241][242] 298 croplands, 77, 79 desert, 77, 79, 85 ice, 77, 79 modification, 74, 77 urban, 77, 84 Albedo effect, 267, 269, 270 Albedo enhancement, 34, 36 Albedo management, 250 Albedometer, 235-236 Alkalinity, 208, 210, 215 Aluminum oxide (Al 2 O 3 ), 91, 93, 94 Alzheimer's disease (AD), 93, 94 Animal UN, 127-128, 130 Animals, 125-131, 135-137 Anthropogenic greenhouse gas emissions (GHG), 247-251, 254, 257-258 Antiracist, 68 Archimedes of Syracuse, 433 Arctic, 99-102, 107 Arctic amplification (AA), 302 Arctic ice cover, 271 blocking wind/water interface, 272 climate model results for short and longwave radiation, 268 keeping heat in Arctic Ocean, 266, 268 removal, 269 salinity, reducing, 272 thickness of, 266 Arctic Ocean surface waters, salinity of benefits and challenges of methodology, Biochar, 258 Biogenic materials, 258 Biomass, 258 BLUHI, 232-233 Bogota, 260 Bretton woods, 146 Bright ice initiative, 292 Bright ocean, 22 Budyko blanket, 368 Budyko-sellers models, 179 Bulawayo, 256 CALIPSO, 297, 300, 302-303 Canada, 97-108 Canopy, 232-233 Carancas crater, 394 Carbon border adjustment mechanism (CBAM), 254 Carbon capture, 80 Carbon capture and storage, 98, 104 Carbon capture sequestration, 34 Carbon dioxide emissions, 79 removal, 73-84 Carbon dioxide removal (CDR), 31, 33-34, 36, 43, 44-45, 99, 207-209, 250, 331, 332, 340-344 Carbon export, 331, 332, 341-343 Carbon offsets, 104 Carbon pricing, 254 Carbon sequestering materials, 249, 258, 261 Carbon sinks, 251, 257 Carbonate, 208, 215 Carbon-negative products, 258 Cars, 251, 252, 257, 259 CFD, 233 Chelyabinsk fireball, 392 Chicxulub crater, 394 Cirrus cloud thinning (CCT), 100, 297-303 Cirrus clouds, 307-308 Climate change, 247-250, 252, 254, 256, 258 mitigation, 73-84 Climate change, impacts of, 266 models, heat balance, 266, 267 Climate contracts, 260 Climate damages, 118-120, 122, 123 Climate emergency, 37-38 Climate hegemon, 147-148 Climate isolationism, 65, 66, 67 Climate litigation, 254 Climate modeling, 290 Climate resilience, 256 Climate target, 102 Cloud experiment, 368 Cloud optical depth (COD), 299-300, 302 Cloud radiative effect (CRE), 297, 299-300, 303 Cloud whitening, 34 Clouds cirrus, 77 maritime, 77 CLUHI, 232-233 CO 2 emissions, reducing, 265-266 CO 2 ionization, 370 Coal, 65 Coatings, 240-241 Collective action problem, 142-143, 148 Collective good, 144, 146, 149 Community aerosol and radiation model for atmospheres (CARMA), 193-194, 196 Compound hazards, 53 Concrete, 231-232, 234-235, 238-242 Conduction heat, 271-272 Consultation, 107 Convection, 237-238, 242 Convection, temperature, 271-272 Copenhagen, 259 Copernicus climate change service, 249 COVID-19, 15 Cretaceous-paleogene boundary, 396 Crisis, ecological climate, 65 Croll-Milankovitch cycle, 424 Curitiba, 260 Daisyworld, 429 DART mission, 401, 435 Death, 65 Debye length, 381 Decarbonization, 247-248, 251, 254, 256, 258-261 Decolonial, 68 Deep multi-agent reinforcement learning (DMARL), 181 Deep reinforcement learning (DRL), 171-173, 176-177, 182, 185 Dementia, 93, 94 Direct air capture, 99 Distributed energy systems, 252, 256, 257, 261 10.1002/9781394204847.index, Downloaded from https://onlinelibrary.wiley.com/
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.651 | 0.446 |
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".