Ground Search and Rescue (GSAR) Baseline Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it