The use of forest maps for the description of wildlife habitats: limits and recommendations
Bibliographic record
Abstract
We evaluated the reliability of forest maps for describing wildlife habitats. During the summer of 1997, we sampled 186 boreal forest stands located in Jacques-Cartier Park, Quebec. In each stand, we measured slope, crown closure, basal area, as well as tree height and age. We determined if map classifications, with regard to dominant species composition, density, tree height, tree age, and slope, correlated with field observations. We also measured lateral cover and deciduous browse availability, variables that are considered useful for the characterization of wildlife habitats, to examine how these habitat features were related to map classification. Age (57% of the sites correctly classified) and density (34%) were the variables for which map classification had the best and worst correspondence with field measurements, respectively. Dominant species on maps were correctly identified in <74, <55, and <40% of the sites in coniferous, mixed, and deciduous stands, respectively. The use of a simple classification method based on cover type alone resulted in improved correlations, since 94, 60, and 29% of the coniferous, mixed, and deciduous stands, respectively, were properly identified on maps. We related lateral cover and food availability to stand categories using the most reliable map variables. We conclude that forest maps are useful for describing major habitats at the stand level. When forest resource maps are to be used for studying habitat suitability, we recommend sampling a subset of stands to assess if important wildlife habitat features, which reflect species requirements, can be related to habitat characteristics as determined by the maps.
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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 | 0.053 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".