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
Lightning strike, fire weather, and fire occurrence data were used to model (i) the probability that a lightning strike causes a sustainable ignition on the forest floor and (ii) the probability of an ignition being detected and reported to the fire management agency for each ecoregion in the province of Ontario. An index that tracks duff moisture content in very sheltered areas of a forest stand (near the tree boles) was the most significant predictor in each ignition model. The presence of positive cloud-to-ground lightning strikes was also found to have a significant and positive influence on the probability of ignition in most areas of the province with the exception of the far northwest. Weather conditions following a lightning storm influence the probability that a lightning strike causes a sustainable ignition. Models of the probability of detecting a fire ignited by lightning were also created for each of the ecoregions across Ontario. The form of these models varied somewhat among ecoregions, but contained an indicator of receptive surface fire spread conditions and an indicator of the dryness of the heavier fuels (the organic layer) in the forest floor.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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