Ground Search and Rescue (GSAR) Baseline Study
Bibliographic record
Abstract
CONTENTS 1. INTRODUCTION 1 1.1. Overview 1 1.2. Objectives 1 1.3. Project management 1 2. DEVELOPING A GEOREFERENCED DATABASE FOR LP-RELATED GSAR IN ONTARIO 2 2.1. Overview 2 2.2. Georeferencing and geocoding principles 2 2.3. Digital geographic data standards 3 2.3.1. Georeferenced federal and provincial data sets 4 2.4. Building a georeferenced database from historical LP-related OPP records 5 2.4.1. OPP records 5 2.4.1.1. Data entry and editing 6 2.4.1.2. Non-spatial data characteristics 7 2.4.1.3. Spatial data characteristics 11 2.4.2. Geocoding LP-related GSAR data in Ontario 12 2.4.3. Mapping out OPP LP-records by census units 14 2.5. Database management 20 3. SPATIAL DATA ANALYSIS 24 3.1. Exploratory spatial data analysis 25 3.2. Bulding statistical models for risk assessment and prediction 28 3.3. Bulding statistical models for resource allocation 34 4. CONCLUSIONS AND RECOMMENDATIONS 41 5. REFERENCES 45 APPENDIX-1. DOCUMENTATION FOR THE LP-RELATED GEO
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".