Predicting tree survival in Ontario sugar maple (<i>Acer saccharum</i>) forests based on crown condition
Bibliographic record
Abstract
Decline index (indicator of crown condition) data from 102 forest plots (approximately 10 000 trees) during 1986–2004 were compiled to derive survival models for south-central Ontario, Canada. The dominant species was sugar maple ( Acer saccharum Marsh.) with approximately 75% occurrence (n = 7640). The predictor variables for sugar maple survivorship included the decline index of 1 or 2 years prior to the beginning of the modelled period and ecological region (Algoma, Georgian Bay, Huron–Ontario, and Upper St. Lawrence). The observed crown condition of sugar maple improved significantly over the study period; in contrast, short-term mortality rate did not improve. The risk of sugar maple mortality could be predicted from decline index data for a single year indicating that the risk of tree death increases with higher decline index values (declining crown condition). Moreover, using 2 years of decline index data indicated that the risk of tree death also increased with the length of consecutive time individual trees have higher decline index values. Trees in the Algoma region, which represent the northern limit of sugar maple distribution in Ontario, were significantly more likely to die than trees in Huron–Ontario region.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".