Successional pathways on different surficial deposits in the coniferous boreal forest of the Quebec Clay Belt
Bibliographic record
Abstract
To infer successional pathways on different surficial deposits in northwestern Quebec, we combined the chronosequence approach to the study of within-stand species' vertical distribution. The deposits sustained different frequencies of postfire stand composition types and successional trajectories. After fire, Picea mariana (Mill.) B.S.P. dominated all stands on organic deposits, while shade-intolerant species dominated the canopy in over 30% and 60% of stands, respectively, on fine- and coarse-textured deposits. In the absence of fire, P. mariana eventually dominates the canopy of all stands on every deposit. However, as the frequency of Larix laricina (DuRoi) K. Koch and Abies balsamea (L.) Mill. in old stands (>100 years) are, respectively, positively and negatively correlated with the deposits' paludification potential, changes in composition in this landscape appear to be driven by the species' tolerance to shade and paludification. Structural changes were characterized by the replacement of closed dense stands by open stands. The diversity of P. mariana stand structures within the youngest age-classes suggests that fire severity may also explain the diversity of stand structures within this landscape. As among-stand structural diversity decreases with increasing time elapsed since fire, converging structural development pathways may be occurring in this landscape.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".