Estimating time since death of <i>Picea glauca</i> × <i>P. engelmannii</i> and <i>Abies lasiocarpa</i> in wet cool sub-boreal spruce forest in east-central British Columbia
Bibliographic record
Abstract
A new method for studying stand disturbance regimes, which could be used alone or combined with other approaches (e.g., age class analysis, tree ring analysis, direct gap measurements), is presented. The method is a set of multiple regression models that estimate the year of death of trees on the basis of external characteristics (e.g., bark presence) and tree position (standing or down). The models were calibrated for Picea glauca (Moench) Voss × P. engelmannii Parry ex Engelm. and Abies lasiocarpa (Hook.) Nutt. trees with known dates of death determined from permanent sample plot data obtained from the Aleza Lake Research Forest, in east-central British Columbia, in the wet cool foothills of the Rocky Mountains. The P. glauca × P. engelmannii model explained 95.3% and 79.3%, and the A. lasiocarpa model explained 81.2% and 78.2%, of the variation in years since death for standing and down trees, respectively. The models were validated by an independent sample of dead trees, where the model estimate was compared with year of release determined from tree ring cores in subordinate understory trees. The two estimates were related (R2 = 61.3%, for both species), indicating that the model provides acceptable estimates for year of death in the two species. This approach may be particularly useful for determining year of death for trees that do not have subordinate individuals that release following overstory mortality.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".