Environmental correlates of canopy composition at Mont St. Hilaire, Quebec, Canada<sup>1</sup>
Bibliographic record
Abstract
Arii, K., B.R. Hamel, and M.J. Lechowicz (Department of Biology, McGill University, 1205 Av. Docteur Penfield, Montréal, QC H3A 1B1). Environmental correlates of canopy composition at the Gault Nature Reserve in southwestern Quebec, Canada. J. Torrey Bot. Soc. 132: 90–102. 2005.—The environmental basis for variation in canopy composition was investigated in an extensive old-growth forest at Mont St. Hilaire, Quebec, Canada. Based on 144 permanent plots, spatial variation in canopy tree species and the effect of environmental variables on canopy composition were examined using canonical correspondence analysis (CCA). Slope and the amount of direct solar radiation received during the growing season, both of which are good indicators of soil moisture, were the main factors explaining plot-to-plot variation in canopy composition. Quercus rubra, Betula papyrifera, Ostrya virginiana and Pinus strobus predominated on plots with high insolation and steeper slope, while species such as Acer saccharum, Fagus grandifolia, Betula alleghaniensis, and Tilia americana occurred on sites with gentler slope and lower insolation during the growing season. Additionally, plots with greater dominance of Acer saccharum in the canopy had higher soil nitrogen availability, and plots with greater dominance of Fagus grandifolia had lower Ca availability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".