A comparison of historical and current forest cover in selected areas of the Great LakesSt. Lawrence Forest of central Ontario
Bibliographic record
Abstract
Crown survey notes from the late 1800s were used to reconstruct forest cover at that time in four forest management units in central Ontario, Canada. Historic forest cover was then compared to forest cover in 1990 based on Forest Resources Inventory (FRI) maps. Regional results indicate that the proportions of maple (Acer spp.) in the forest increased by 12.5%, while balsam fir (Abies balsamea) declined by 3.5%, hemlock (Tsuga canadensis) by 2.3% and other conifers (larch (Larix laricina) and cedar (Thuja occidentalis)) by 2.1%. The frequency of occurrence of maple, ash (Fraxinus spp.), yellow birch (Betula alleghaniensis), poplar (Populus spp.) and spruce (Picea spp.) also increased while white birch (Betula papyrifera), hemlock and other hardwoods (e.g., oak (Quercus spp.), basswood (Tilia americana), beech (Fagus grandifolia), elm (Ulmus spp.), ironwood (Ostrya virginiana) and black cherry (Prunus serotina Ehrh.)) declined. The region-wide proportional increase in maple is likely due to timber harvest techniques such as selective logging, effective fire suppression and the ecology of the maple species. Crown survey notes have been a useful tool in reconstructing presettlement forest cover. Survey notes can easily be obtained and used by forest managers and planners to understand presettlement conditions of this forest. Managers can achieve zero net loss of forest types in relation to the presettlement condition by using appropriate silvicultural practices to reduce the proportion of maple. Key words: Crown Survey records, presettlement forest, Great LakesSt. Lawrence Forest, working group, frequency of occurrence
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".