Bibliographic record
Abstract
absolute pressure, 3 achieving sustainability, 203 acid rain, 194 active cooling, 514 active heating, 514 advanced applications, 542 air quality management, 204 analysis, 270, 316 applications, 198 aquifer, 118 aquifer storage, 118, 272 artificial neural network, 560 ASHRAE standards, 104 ATES, 272 atmospheric pressure, 3 auxiliaries, 529 balance equations, 235 battery, 63 benefits, 89, 183 Bernoulli's equation, 23, 26 biological storage, 75 Biot number, 32 borehole, 466, 471 borehole thermal energy storage, 466, 471 boundary layer, 29 Boussinesq model, 344 Brundtland Commission, 200 Brundtland Commission's definition, 200 building applications, 107 capsule, 136 case study , 204, 225, 277, 304, 349, 369, 376, 413, 414, 432, 436, 446, 455, 457, 459 challenges, 105 change of state, 9 charging efficiency, 263 charging period, 263
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.678 | 0.582 |
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".