Preindustrial reconstruction of a perhumid midboreal landscape, Anticosti Island, Quebec
Bibliographic record
Abstract
The knowledge of natural disturbance dynamics and preindustrial landscapes is essential to implement sustainable forest management. Recent findings identify the lack of a forest dynamics model, different from the standard cyclic model of Baskerville (1975. For. Chron. 51: 138–140), for balsam fir ( Abies balsamea (L.) Mill.) ecosystems of maritime eastern Canada. With the use of historical forest maps and dendrochronology, we reconstructed the range of variability of the preindustrial landscape (6798 km 2 ) and inferred on the natural disturbance dynamics of the balsam fir forest of Anticosti Island. The preindustrial landscape was characterized by a forest matrix of overmature softwood stands with inclusions of younger softwood stands ranging from 0.1 to 7837 ha in size. Widespread stand-initiating events were apparently rare in the preindustrial landscape over the last 160 years. Since our results were not well represented by the cyclic model, which predicts the occurrence of a mosaic of stands in different age classes, we proposed an alternative forest dynamics model for eastern balsam fir ecosystems near the Gulf of St. Lawrence. Forest management inspired by this alternative model may be more appropriate to maintain or restore ecological characteristics of balsam fir forests of this region within their range of natural variability.
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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 | 0.001 | 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".