Relating changes in duff moisture to the Canadian Forest Fire Weather Index System in <i>Populus tremuloides</i> stands in Elk Island National Park
Bibliographic record
Abstract
The manner in which trembling aspen ( Populus tremuloides Michx.) forest duff moisture changes during the growing season was investigated in Elk Island National Park, Alberta, Canada. A calibration–validation procedure incorporating one calibration site with moisture sampling across three topographic positions was used to develop predictive models, which were subsequently compared with 12 validation sites across three vegetation types throughout the Park. Duff moisture was modelled against the Duff Moisture Code and Drought Code components of the Canadian Forest Fire Weather Index System. Spring, summer, and fall rates of duff moisture change differed (P < 0.050) during calibration, with moisture loss greatest in spring. Additionally, while moisture changes on the south-facing and crest topographic positions were similar during spring, moisture losses were greater (P < 0.050) at these locations compared with the north-facing landscape position. Correlation analysis indicated that duff inorganic content and bulk density were both related to duff moisture but were limited in importance compared with weather-based influences. When compared with predicted values obtained from calibrated models, moderate predictability of duff moisture was found (mean absolute error = 20.7%–54.2%). Relative to the national standard equations, unique but very different empirical relationships were developed between the Duff Moisture Code and Drought Code and the moisture content of the duff layer in aspen forest stands found in Elk Island National Park.
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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 | 0.001 |
| 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 | 0.001 | 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".