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
Fire investigation is arguably one of the most difficult areas of investigation. The fire scene and available evidence has often been burnt, melted, smoke-stained, water-damaged and trampled on, but the fire investigator still has to make important distinctions between whether a fire was accidental or deliberate (arson). Modern fire investigations often rely on portable electronic detectors to identify ignitable liquid residue (ILR), or accelerant detection canines (ADCs), trained on a number of target substances. An analysis of cases from England and Wales, the United States of America (USA) and Canada demonstrates that sophisticated admissibility frameworks have not been effective in rejecting opinion testimony given by investigators and dog handlers that unconfirmed dog alerts where laboratory tests were negative provided proof of arson. This is problematic and controversial, and the authors conclude that such testimony is not compatible with modern forensic or scientific standards and should not be admitted into courts.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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