Risk of extirpation for vertebrate species on an industrial forest in New Brunswick, Canada: 1945, 2002, and 2027
Bibliographic record
Abstract
The risk of extirpation was assessed for 157 vertebrate species for a ca. 190 000 ha forest in New Brunswick, Canada, based on land cover in 1945, 2002, and 2027. Data from 1945, prior to intensive forest management, were derived from detailed spatial 19441947 timber-cruise data and maps. Extirpation risk was determined by species, using a categorical system called the species-sorting algorithm whereby each species was assigned to one of four risk classes based on four variables: potential abundance, proportion of the landscape suitable for occupancy, species-specific habitat connectivity, and population growth potential. Data for these variables were derived from species-specific spatial landscape assessments and published life-history parameters. Forest management from 1945 to 2002 decreased the mixed hardwoodsoftwood forest area from 37% to 18%, increased the area of tolerant hardwoods from 10% to 25%, and decreased the area of forest >70 years old from 86% to 44%. Projections for 2027 showed further declines in old softwood, hardwood, and mixedwood habitats. Twenty-seven vertebrate species were ranked as class I (highest extirpation risk) in 1945 versus 20 in 2002 and 26 in 2027; 35 species (22%) were ranked as class I at least once and 15 species in all 3 years. Sensitivity analyses demonstrated that habitat availability was the most important ranking variable for determining extirpation risk, and that changes in habitat threshold values for assigning risk scores significantly altered results. The forest was less sensitive to habitat thresholds in 1945 than in 2002 or 2027 because of greater homogeneity. Low cover of old-forest habitat, especially mixedwood in large patches with adequate connectivity, resulted from both management and natural disturbances, and was the primary factor determining extirpation risk for vertebrates on the landscape.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".