Predicted and Observed Sugar Maple Mortality in Relation to Site Quality Indicators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In response to high mortality rates after selection cutting, the Quebec Ministry of Natural Resources developed a new tree classification system, named MSCR, to better identify trees with high mortality probabilities. The main objective of this article was to verify whether sugar maple mortality is more abundant on poor quality sites than on rich quality sites using (1) sample plots, measured only once, in which mortality is predicted using MSCR, and (2) remeasured sample plots that provide a real cumulative 10-year mortality assessment. The results presented show that sugar maple predicted mortality (based on MSCR) is greater on good sites than on bad sites. This result is in contradiction with our 10-year mortality results and the literature. Combining strong components of MSCR with those of other systems described in the literature, we put forward the conceptual basis for a new classification system for the northern hardwoods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it