[Prediction of litter moisture content in Tahe Forestry Bureau of Northeast China based on FWI moisture codes].
Bibliographic record
Abstract
Canadian fire weather index system (FWI) is the most widely used fire weather index system in the world. Its fuel moisture prediction is also a very important research method. In this paper, litter moisture contents of typical forest types in Tahe Forestry Bureau of Northeast China were successively observed and the relationships between FWI codes (fine fuel moisture code FFMC, duff moisture code DMC and drought code DC) and fuel moisture were analyzed. Results showed that the mean absolute error and the mean relative error of models.established using FWI moisture code FFMC was 14.9% and 70.7%, respectively, being lower than those of meteorological elements regression model, which indicated that FWI codes had some advantage in predicting litter moisture contents and could be used to predict fuel moisture contents. But the advantage was limited, and further calibration was still needed, especially in modification of FWI codes after rainfall.
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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 | 0.000 | 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 | 0.000 | 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".