Can the Canadian drought code predict low soil moisture anomalies in the mineral soil? An analysis of 15 years of soil moisture data from three forest ecosystems in Eastern Canada
Bibliographic record
Abstract
Abstract The Canadian Drought Code (CDC) is an empirical soil‐drying model adapted to high‐latitude forests and commonly used by Canadian fire managers and researchers to predict the water content of the organic soil layer. Better knowledge of the capacity of the CDC to predict the effect of droughts on the water content of the mineral soil could improve our capacity to predict the future response of Canadian boreal forests to future changes in drought frequency and intensity. We tested the capacity of the CDC to predict mineral soil water content (SWC) and droughts against long‐term (14–16 years) daily mineral SWC data from time domain reflectometry probes in multiple stations within three forest ecosystems of Eastern Canada respectively dominated by sugar maple, balsam fir and black spruce. Droughts were defined as SWC values lower than one standard deviation from their historical mean. The drought intensity and frequency of each growing season were computed as the sum of daily SWC departures from normal and the sum of days of drought, respectively. Our results show that the CDC is a reliable predictor of mineral SWC (r = 0.6–0.8), drought frequency (r = 0.5–0.9) and intensity (r = 0.7–0.9) for high‐latitude forest ecosystems of Eastern Canada. Lower correlations were due to the poor accuracy of the model at predicting mild droughts at the sugar maple stand due to the SWC values close to the drought threshold. We detected a higher susceptibility to droughts at the black spruce stand due to a 1‐month‐earlier occurrence of severe droughts. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".