An attempt to explain the distribution of the tree species composing the riparian forests of Lake Duparquet, southern boreal region of Quebec, Canada
Bibliographic record
Abstract
The objective of this study was to evaluate the most important environmental factors determining the distribution of tree species within the riparian zone of Lake Duparquet, located in the southern boreal region of Quebec, Canada. Occurrence and relative basal area of 10 species were recorded within an altitudinal range of 200 cm above mean water level along 95 transects. Stepwise logistic regression and canonical correspondence analyses were performed on the overall data set as well as separately for the five geomorphological shore types distinguished (depositional flats, floodplains, beaches, terraces, and rock outcrops). The elevation gradient, representing seasonal floodings, is the main factor determining the distribution of the species. The differences between the geomorphological shore types with respect to composition and arrangement of the arborescent vegetation along the elevation gradient are at least partially explained by surficial substratum, topography, aspect, and fire. Exposure to wave activity seems to be of minor importance only. However, since they are the driving force of erosion and sedimentation, the waves are to a great part responsible for the morphological differentiation of the shoreline. The distribution of the tree species along a characteristic physiographic cross-section is illustrated for each geomorphological shore type.
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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 | 0.001 | 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) | 0.002 | 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".