Pre‐industrial landscape composition patterns and post‐industrial changes at the temperate–boreal forest interface in western Quebec, Canada
Bibliographic record
Abstract
Abstract Questions What were the pre‐industrial forest landscape composition patterns? Which factors had structured the pre‐industrial landscape patterns? How have pre‐industrial landscape patterns and post‐industrial disturbances controlled composition changes? Location An area of 4175 km 2 at the temperate–boreal forest interface of southwest Quebec, Canada. Methods Reconstruction of the pre‐industrial composition is based on an original early land survey data set (1874–1935). Composition changes were computed by comparing historical data with modern forest inventories. Landscape‐scale patterns and composition changes were assessed through spatially constrained clustering analysis. Results Pre‐industrial forest composition was structured across the landscape by the combination of environmental gradients (topography, deposits, drainage, etc.) and recurrence of fire. Frequency and intensity of fires were most likely the main drivers of forest dynamics and composition across the landscape. Black spruce ( Picea mariana ) and balsam fir ( Abies balsamea ) dominated hilly areas affected by former fires; aspen ( Populus tremuloides ) dominated lowlands following recent fire. White cedar ( Thuja occidentatlis ) and pines ( Pinus spp.) dominated areas probably affected by small surface fires. New disturbance regimes that were subsequently incurred by human activities have shifted the pre‐industrial landscape mosaic and have led to the current landscapes. Composition changes included a replacement of conifers by early successional species within settled or burned areas, and the maintenance of conifers and an increase in cedar dominance in areas affected by partial disturbance. Conclusions Post‐industrial composition changes must be perceived as complex interactions between pre‐industrial landscape patterns and natural and human disturbances. Such land‐use legacies could be important drivers of future landscape change and should be investigated and considered when predicting future climate‐induced ecological changes.
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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 | 0.003 |
| 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) | 0.003 | 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".