Predicting forest floor moisture for burned and unburned Pinus banksiana forests in the Canadian Northwest Territories
Bibliographic record
Abstract
We assessed how well the fuel moisture codes of the Canadian Forest Fire Weather Index System can be used to predict forest floor moisture in burned and in mature, unburned jack pine (Pinus banksiana Lamb.) stands in Canada’s Northwest Territories. Moisture content sampled at varying depths in the forest floor profile was compared with hourly variations in the Fine Fuel Moisture Code and daily variations in the Duff Moisture Code and Drought Code. Fuel moisture samples were collected in June 2000 and August 2002 from one mature forest stand and four experimental plots that were burned between 1997 and 2000. Forest floor moisture, drying rate, and water-holding capacity varied between the mature, unburned forest and burned plot areas, as well as between surface and subsurface fuel layers within the mature forest. The Duff Moisture Code was the best predictor of forest floor moisture for all the fuel components examined, based on the coefficient of determination (r2 = 0.81–0.90) and because its relationship with forest floor moisture more closely resembled relationships observed in previous studies.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".