Bibliographic record
Abstract
Accident investigation method, 248-249 Action Plan for Beautiful and Clean Malaysia (ABC), 89 Administrative incentive (see Structural incentives) Administrative incentives, 41 Affordable high-rise public housing, 181 Affordable house, 180, 181-182 Apache, 112 Aperture, 276 Approval Permit system (AP system), 221 ASEAN Agreement on Disaster Management and Emergency Response (AADMER), 18 Asian Disaster Preparedness Center (ADPC), 21 Asphyxiation, 159 Association of Consulting Engineers Malaysia (ACEM), 210 Association of Southeast Asian Nations Countries (ASEAN Countries), 17-18 DRA in, 19-24 DRM in, 18-19 INDEX non-numerical data process, 153 OSHCIM, 151 qualitative and quantitative methods, 152-153 statistic of fatal accidents, 153-154 sustainable development, 150 Centre for Excellence in Disaster Management and Humanitarian Assistance (CEDMHA), 22 Centroid method, 78 Certification Service Technician Programme (CSTP), 227 Chinese standard, 280 Chlorofluorocarbons (CFCs), 220 Cleat attribute, relationship between permeability and, 270-272 Cleat distributions and orientations, 266 Cleats orientation, 276 'Climate capitalism', 6 Climate change, 9 Climate Change Economics Assessment, 4 Cluster, 8 Coal rank, 268 Coal sampling, 268 Coalbed methane (CBM), 266 Coefficient of performance (COP), 221 Communication on construction site, 244 Complete stress-strain curve, 289 Compressed natural gas (CNG), 5 Computable General Equilibrium (CGE), 9 Computer-generated Imagery (CGI), 250-251 Concrete, tensile strength of, 280 Consistency ratio (CR), 140 Construction claim attributes, 48, 58-60 claims, 46, 47 critical claim attributes, 49-51 Cronbach's alpha coefficient, 49
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.765 | 0.805 |
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".