Predicting duff moisture in a boreal forest ecosystem at various retention levels
Bibliographic record
Abstract
The Canadian Fire Weather Index (FWI) system is used across Canada and worldwide to provide numerical ratings of fuel moisture based on the fine fuel moisture code (FFMC), duff moisture code (DMC) and drought code (DC). DMC is related to dryness of the duff layer. While DMC has been widely calibrated and validated in different stand types, it has not yet been calibrated for retention harvesting sites in the boreal mixedwood landscape of north-central Alberta. The objective of this research was to explore whether duff characteristics (duff load) and stand parameters (leaf area index, basal area) could be used in predicting duff moisture and whether the standard-DMC estimated by the FWI system matches with field-DMC. This study was conducted in conifer-dominated mixedwood stands that had received a range of variable retention harvesting in 1998/1999 (clear-cut, 20%, 50% , 75% - retentions and control) as part of the EMEND research project near Peace River, Alberta. Duff moisture, duff characteristics and vegetation parameters were measured in the field and DMCs were estimated for June, July and August in 2014 across retention levels. A trenching experiment was conducted to see if transpiration losses were related to duff moisture across retention levels. The results indicated that duff characteristics were influenced by litter deposition during harvesting and addition of fresh leaf litter from regenerated aspen. Duff moisture was influenced by slope and elevation more than species composition. Among the duff variables, duff load was a better predictor of duff moisture (R2=0.60). A three-way ANOVA revealed that standard DMC-MC relationships underestimate both field and sensor DMC in June and July.
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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 | 0.000 |
| 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 | 0.001 | 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 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".