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.
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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".