Can Postharvest Regeneration in Group Selection and Patch Cutting Predict Future Species Composition?
Bibliographic record
Abstract
In Quebec, Canada, multicohort forest management in hardwood and mixedwood stands include group selection cutting and patch cutting.We assessed the success of these cuts in regenerating the intermediate shade tolerant yellow birch (Betula alleghaniensis Britt.; YB) at an operational scale and over a large territory using surveys conducted at 2, 5, 10, and 15 years after harvest.Regeneration of the target species was successful, with YB showing a mean stocking around 60 percent and a mean sapling density around 3,400 stems ha -1 after 15 years.The relative presence of YB in 15-year-old canopy openings-a proxy for future species composition-was best predicted by that species' relative abundance, stocking based on one stem per sampling unit, and mean maximum height measured in year five (rather than year two) using smaller sampling units (6.25 m 2 rather than 25 m 2 ).See Bilodeau-Gauthier et al. (2020) for further details.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".