Bibliographic record
Abstract
information modelling (BIM) adoption, 171-173 advantages of, 171 awareness and utilisation, 181-183 concepts of, 173-174 definition, 173-174 efficiency, 170 India, 177 Nigeria, 171-172 origin, 173-174 productivity, 170 sustainability, 170 technology of, 170-171 utilisation, 171, 183 Building Research Establishment Environmental Assessment Method (BREEAM), 21, 27, 175-176 Canada, 171 Catholic Agency for Overseas Development, 116-117 China air pollutants emission, 145 air quality, 145 annual urban population growth rate, 144 Beijing Capital International Airport, 143 city of Jining, 93, 96 COVID-19.See COVID-19 eco-cities, 84-86, 92-93 ecosystem evaluation, 146 environmental externalities, 145-146 governmentality, 92-93 gross domestic product (GDP), 143 hedonic price models, 147 individualisation, 91 log-linear, 148 North Lake Ecological New Town (NLENT), 87 pollution treatment, 144 urban and rural population, 142 Urban Green, 148 China General Nuclear Power Corp (CGN), 56-57 Chi-square (x 2 ) statistics, 15-16 City of Jining, 93-96 City of Ndola, 65, 70-72 Civil society organizations (CSOs), 119 Climate change, 3-4, 117, 120-121 global agreement, 117 global warming and, 121 greenhouse gas (GHG) emissions, 120-121 CO 2 emissions, 130 Computer-aided design (CAD), 173 Construction, demolition and excavation (CDE), 202-203 Construction quality, 18 Control systems, 197 Cost effectiveness analysis, 28-34 COVID-19 aviation industry, 213-214 banking sector, 215
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.727 | 0.771 |
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".