Gradient analysis of <i>Larix laricina</i> dominated wetlands in Canada's southeastern boreal forest
Bibliographic record
Abstract
With the objective of understanding how vegetation was structured in four Larix laricina (Du Roi) K. Koch dominated wetlands in north-western Quebec, 186 point-centred quarters were sampled in four stands. For each point, both biotic and abiotic variables were collected and species cover was recorded. Divisive hierarchical classification analysis (Twinspan) identified nine vegetation clusters: i) Larix laricina & Spiraea alba, ii) Larix laricina & Kalmia angustifolia, iii) Larix laricina, Picea mariana & Alnus rugosa, iv) Larix laricina & Betula pumila, v) Thuja occidentalis & Trientalis borealis, vi) Abies balsamea & Betula papyrifera, vii) Fraxinus nigra & Onoclea sensibilis, viii) Alnus rugosa, and ix) Eleocharis smallii. Results of the canonical correspondence analyses indicated that the distribution of these clusters was mainly related to (i) distance from shore, (ii) shade (canopy cover), (iii) substrate nitrate concentration (in relation to the abundance of Kalmia angustifolia and Alnus rugosa), (iv) substrate pH (in relation to the abundance of Sphagnum spp.), and (v) substrate conductivity. Several characteristics of the water table also affected species distribution, including pH, depth, and carbon concentration. Further studies should address the effect of the presence of Kalmia angustifolia and Alnus rugosa on larch growth.Key words: larch, wetland, vegetation analysis, flooding, boreal forest.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".