A Mapping of the Present and Past Forest-types of Prince Edward Island
Bibliographic record
Abstract
Our aim was to produce maps showing the distribution on Prince Edward Island of five forest-types previously identified from a TWINSPAN analysis of ground flora data collected at 1200 sampling points in a field survey. For this purpose we had available two databases: one on the composition of the tree canopy of 82,957 forest stands, as determined by photointerpretation of a 1990 aerial photographic survey of the island; the other on the drainage properties of the same stands from a published soil survey. The tree canopy and drainage criteria for sorting these stands into five stand-types were chosen in the light of the equivalent properties of the TWINSPAN forest-types as evident from the field survey. These criteria were perfected in four trial computer-sortings, followed by the computer-printing of maps showing the distribution of the standtypes. These maps, which were then evaluated by comparing them with the properties of the TWINSPAN forest-types, are the first fine-scale maps of the main forest-types of the island. They reveal that, of the three “primary” forest-types, the upland hardwood forest occurs especially in the central and south-eastern hill-lands, as well as in scattered parcels elsewhere, whereas the Black Spruce forest and the wet species-rich woodland occur primarily in areas of lower elevation in the east and west of the island. The two forest-types resulting from human disturbance, the White Spruce woods and the “disturbed forest”, have a more scattered distribution, with the White Spruce woods being found especially in the central and eastern parts of the island and the disturbed forest in the west and east of the island. A secondary aim was to map the conjectured distribution before European settlement of the three primary forest-types: two maps have been produced, one showing the distribution of upland hardwood forest, the other of the wet forest-types.
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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 | 0.001 |
| 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) | 0.002 | 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".