Bibliographic record
Abstract
process), 392 Administrative units, 265 Agent-Based (AB) approaches, 301, 334 Agent-based (AB) modeling/simulation, 32-34, 301, 309 Agent-based epidemiological approach, 309 Agent-based model (ABM), 14, 31-33, 309, 414-416, 443, 447-449 Age-sex group, 252 Age-structured culling, 64 Airport catchment area (ACA), 99, 100, 102 Alignment-free methods, 61 Analytical models, 301 AnyLogic, 449-450, 455 Arbitrarily shaped clusters, 179, 182, 183, 186 ArcGIS, 167, 236, 250 Asymptomatic infection, 103, 104 Average nearest neighbor distance, 167, 169, 171 (Tab.)Avian influenza, 11, 137-139, 145, 149, 163, 167 Avian influenza, ecology, 138 Avian influenza, epidemiology, 138 Backward bifurcation, 63 Bayesian approach/framework, 13, 26, 194-195, 199-201, 217-230, 233-243 Bayesian credible intervals (BCIs), 238 Bayesian inference, 233-243 Bayesian spatial models, 201, 218-220 Bayesian spatiotemporal geostatistics, 194, 195 Borrelia burgdorferi bacteria, 331, 371, 375 Buffer, 265 C programming language, 106 C++ geosimulation system (geosimulator), 312, 334, 342, 344, 362-366 C++ programming language, 254 Calibration(model), 273 CBR data, 285-286 comandra blister rust (CBR), 285 disease host plants, 286 lesion, 285 lodgepole pine, 285 Cell phone data, 444-447, 454 Cellular Automata approach, 308-309, 318, 313, 334, 415 Cellular Automaton (CA), 29, 308 Census dissemination areas (DAs), 180 Census metropolitan areas (CMAs), 180 Census tracts (CTs), 180 Chem-bioinformatics, 55 Classification and Regression Trees (CART), 195 Classification trees, 195 Climatic scenario, 300 Clinical diagnosis, 54 Clinical results, 150 Close Proximity Data Source, 465 Cloud computing, 182 Cluster centroids, 169, 172 (Fig.) Cluster paths, 169 (Fig.) CODIGEOSIM project, 312 Analyzing and Modeling Spatial and Temporal Dynamics of Infectious Diseases, First Edition.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.629 | 0.515 |
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".