Identifying growth releases in dendrochronological studies of forest disturbance
Bibliographic record
Abstract
Information on historical disturbances is vital to our understanding of current forest conditions. Dendro chronological methods provide one means of reconstructing disturbance histories in temperate and boreal forests. In particular, the dates of significant growth releases recorded on surviving trees provide strong inferential evidence of past disturbance events. The most common method of detecting releases (the percent-increase method) expresses the postevent growth increase as a percentage of the preevent rate. Despite its widespread use, the method is known to be overly sensitive at low rates of prior growth and overly stringent at high rates. We present an alternative method that directly follows the percent-increase method, but instead of dividing the postevent growth rate by the preevent rate, we simply subtract the two. If the difference exceeds a predetermined species-specific threshold, the event is considered a release. This absolute-increase method has convenient properties that remedy the shortcomings of the percent-increase method. We tested the validity of the absolute-increase thresholds by binary logistic regressions, and we compared the absolute- and percent-increase methods by various methods. We conclude that for the species evaluated in this study, the absolute-increase method represents an improvement over the standard percent-increase method.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".