Bibliographic record
Abstract
absorptive capacity 23 agriculture 97 Ajzen, Icek theory of planned behaviour 326 theory of reasoned action 326 Akaike information criterion (AIC) 207 ambidexterity 23 Apple, Inc. product lines of 326-7 Aristotle 228 Audi 324, 333 Australia 200 New South Wales 201 Bayesian information criterion (BIC) 207 Berkeley Lab 128 Berners-Lee, Tim Director of W3C 363 role in development of Semantic Web 363 binomial regression negative 201 bioethics 236 BioHeat Alternative Heat 181 biological systems 232, 236 biotechnology 4, 106, 157-8, 223, 230, 238 as boundary object 222, 224, 234-5 communication in 222-3, 238 companies 121, 139 concept of 3, 225-7 definitions of 223, 225 development of 225, 233-5, 238 early applications of 227-9, 235 genetically modified organisms (GMOs) 236 microbiology 228 output potential of 232 recombinant DNA technology 221, 229-30 synthetic biology 221-2, 224-5, 230, 232, 234-5, 237-9 XNA integration 236-7 BMW AG 333 Brazil 264, 271 business intelligence 93 Cambridge Econometrics energy-environment-economy model (E3ME) 267 Canadian Survey of Innovation (1999) 269 cancer therapy 117, 127-8, 140 Bragg peak 127-8 particle therapy 117, 125-6, 130, 137-9 development of 112 capital human 73, 138, 314 intellectual 64 socio-economic 311 carbon dioxide (CO 2 ) emissions 265, 269 Chamber University indicator-based tool 103 Chiba University 127, 132, 137 China 264, 271 Cisco Systems 54 climate change 262 carbon credits 265, 273 Certified Emission Reduction (CER) credits 270 Clean Development Mechanism (CDM) 262, 265, 270-72 Emission Reduction Units (ERUs) 270 Feed-in tariff (FIT) 272-6 renewable energy certificates (RECs) 272-3
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.721 | 0.561 |
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".