Changes to preindustrial forest tree composition in central and northeastern Ontario, Canada
Bibliographic record
Abstract
Preindustrial forest composition for >180 000 km2 throughout central and northeastern Ontario was recreated from Ontario Crown land survey notes (1816–1955) and compared with existing forest composition derived from current Forest Resource Inventories (1998–2009) in each of Site Regions 3E, 4E, and 5E. A validation analysis was performed using the Forest Resource Inventory data to test the assumption that sampling the land survey tree species composition along township boundaries is adequate in describing the composition of the whole forest. The majority of tree species in each of the three site regions validated successfully. A binary logistic regression model allowed birch genera to be classified at the species level to aid in the interpretation of survey notes. All analyses showed significant reductions in conifers (especially red pine ( Pinus resinosa Ait.), white pine ( Pinus strobus L.), and eastern larch ( Larix laricina (Du Roi) K. Koch)) and significant increases in maple ( Acer spp.), oak ( Quercus spp.), white birch ( Betula papyrifera Marsh.), and poplar ( Populus spp.).
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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 | 0.002 |
| 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) | 0.002 | 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".