The forests of Prince Edward Island: A classification and ordination using multivariate methods
Bibliographic record
Abstract
The forests of Prince Edward Island: A classification and ordination using multivariate methods.Canadian Field-Naturalist 116(4): 585-602.In 1991 data were collected on the ground flora in 1200 4 m? plots at 240 randomly selected forest sites on Prince Edward Island.The nearby trees and shrubs, and attributes of the surrounding forest stand were also recorded and soil samples were collected for analysis.Additional topographic, historical and soil data for each sampling point were obtained from maps and a 1935 aerial photographic survey.1127 of the plots were subjected to multivariate analysis (a TWINSPAN classification and a DECORANA ordination).On the basis of their ground flora composition, TWINSPAN divided the plots into eleven ground flora community-types, which after further comparative analysis of their tree canopies, stand properties and environmental factors, were assigned to five forest-types, each characterised by particular tree species and soil drainage properties: (1) a wet species-rich woodland, (2) upland hardwood forest, (3) Black Spruce forest, (4) old field White Spruce woods, and (5) disturbed, mainly conifer-dominated, forest.The first three were considered to be heavily modified descendants of pre-settlement forest-types, while the two latter appeared to be largely the products of successional processes resulting from human disturbance and forest clearance.The ordination indicated that the most important factors responsible for the ground flora communities and forest-types on Prince Edward Island are soil drainage and human-associated disturbance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".