Rare forest types in northeastern Ontario: a classification and analysis of representation in protected areas
Bibliographic record
Abstract
Our objective was to assess the relative rarity and representation within protected areas of Standard Forest Units (SFUs) in northeastern Ontario by applying the concepts of geographic range, habitat specificity, and local population size. SFUs are stand type classifications, routinely employed by forest managers, based on tree composition, disturbance history, and prescribed silvicultural system. We identified several SFUs as rare because of a narrow distribution, association with only one landform type, or lack of at least one stand larger than an ecoregion-specific threshold. In the Boreal forest, rare SFUs comprised stands dominated by eastern hemlock ( Tsuga canadensis (L.) Carrière), red oak ( Quercus rubra L.), yellow birch ( Betula alleghaniensis Britt.), or eastern white-cedar ( Thuja occidentalis L.). Rare SFUs also included eastern white pine ( Pinus strobus L.) and (or) red pine ( Pinus resinosa Ait.) leading stands managed by shelterwood or seed tree silviculture as well as low-lying deciduous stands and selection-managed stands of shade-tolerant species. In the Great Lakes – St. Lawrence forest, rare SFUs were yellow birch stands, stands dominated by conifer species abundant in the Boreal, and shelterwood-managed hardwood stands. Several rare SFUs had <12% of their total area in protection, i.e., stands dominated by eastern white pine, yellow birch, eastern white pine – red oak, or eastern white-cedar. These rare stand types require increased protection in reserves and tailored silvicultural practices to maintain their probability of persistence.
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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".