A new dendroecological method to differentiate growth responses to fine-scale disturbance from regional-scale environmental variation
Bibliographic record
Abstract
A new dendroecological method is developed to differentiate growth responses to fine-scale disturbance from regional-scale environmental variation. In spruce–fir forests of central British Columbia, release from suppression in response to overhead canopy tree mortality was calibrated as >60% change in radial growth (%CRG, adjacent 15 year periods compared) using gap-maker–gap-filler pairs with known years of mortality and response. Many release events, attributed to regional-scale environmental variation (e.g., bark beetle outbreaks), were counted. Species-specific regional-scale chronologies were subtracted from standardized gap-filler series producing residuals and 1 was added to all residual indices. Percent divergence (%DIV) values were calculated as the percent change in residuals (adjacent 15 year periods compared). A %DIV criterion was set at >15% increase in the residual series. The %CRG and %DIV criteria were applied to an independent data set of ring-width series, determining the date(s) of release for each tree. %CRG and %DIV criteria were used in a complementary approach to differentiate (i) release due to fine-scale canopy gaps, (ii) no response to a gap and regional-scale environmental variation, (iii) release due to regional-scale environmental variation, and (iv) response to a fine-scale canopy gap but not detected by the %CRG criterion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".