Bibliographic record
Abstract
in region, 286-289 circularity for global commodities, 292-296 lessons learnt in implementing CE actions, 296-300 national food waste initiative, 289-292 Agricultural workers, 293 Agriculture, 34-36 "Airbnb" platform, 315 American Transportation Research Institute (ATRI), 158 Analytical models, 219 Apple, 157 Arts & Humanities Citation Index (A&HCI), 219 Asia-Pacific Economic Cooperation (APEC), 243 Automotive supply chain, 82-84 Business models (BMs), 90 (see also Circular business models (CBMs)) as focal point of circularity on company level, 91-93 Business(es), 153, 201 ecosystems, 90 strategies, 284 value and impact, 285, 299 Butterfly diagram, 73-74 By-product synergies, 33 Cannibalism, 32 Carbon dioxide emissions (CO 2 emissions), 243 Carbon dioxide equivalents (CO 2 e), 112-114 competitive recycling and manufacturing locations in very low-cost and CO 2 e CLSCs, 121-124 effects of recycling locations on, 116 Carbon oxide emissions (CO emissions), 243 Case study, 317 Centre for Technology Transfer in Industrial Ecology (CTTÉI), 36 Channels, 93 Charnes-Cooper-Rhodes model (CCR model), 243, 246 China Excess Inventory Circulation Association, 177 China's General Administration of Quality Supervision, Inspection and Quarantine, 177 China's Law for the Promotion of the Circular Economy, 177 China's Standardization Administration, 177 Chinese government, 177 Circular approaches, 71 Circular bioeconomy, 275 Circular business, 219 Intensifying loops, 159 International Platform of Insects for Food and Feed (IPIFF), 273 International secondary markets, 176-179 Internet of Things (IoT), 190, 192 Intricate production networks, 75 Ioncell®, 350-351 Jointness of interests, 130 Karo Sambhav-Microsoft partnership, 39 Key activities, 92 Key partnerships, 92
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.726 | 0.756 |
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".