Under Our Nose: The Use of<scp>GIS</scp>Technology and Case Notes to Focus Search Efforts
Bibliographic record
Abstract
Missing person searches can entail much time, effort, and resources. With scientific data and techniques increasingly available to law enforcement and investigations units, can these tools be used to predict likely areas where persons or remains may be discovered, especially in cases where little to no information about the disappearance is available? A retrospective study of seventy-three (73) cases was conducted in Onondaga County, New York, U.S.A., in order to explore this question. Quantitative (geospatial) and qualitative (investigator notes) data were utilized to determine whether patterns exist that may assist in investigations of recent and "cold" missing person cases. Results showed a majority of cases with relative proximity (<5 miles) between victim last seen (VLS) and body recovered (BR) locations. Furthermore, investigators' notes demonstrated repeated descriptors reflecting natural or cultural features associated with hidden, clandestine provenance (e.g., near bodies of water, wooded areas). With future external validation of this study, consistent priority areas may be identified as foci of searches; these priority areas ideally should be thoroughly checked/cleared before the search zone is expanded.
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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 | 0.032 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".