Changes to preindustrial forest tree composition in central and northeastern Ontario, Canada
Bibliographic record
Abstract
Preindustrial forest composition for >180 000 km 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".