Comparison of forestry-based remote sensing methodologies to evaluate woodland caribou habitat in non-forested areas of Newfoundland
Bibliographic record
Abstract
Forest inventory maps and a manual interpretation of forestry-enhanced Landsat imagery are compared to the results of a detailed aerial photograph interpretation used to map habitat for caribou (Rangifer tarandus terra novae) in a relatively unforested region of Newfoundland. This comparison serves as an illustration of the pitfalls inherent in using readily available remote sensing technologies in applications for which they were not intended. The non-forest classes in the Newfoundland Forest Inventory are too broad to describe single vegetation communities, and only rarely are vegetation communities found entirely within a single forest inventory class. For example, "bog" is relatively well associated with wetland vegetation classes and "barren" with upland classes, but "scrub" is a misleading term used to describe both forest and non-forest communities. An earlier (global) forest classification for Newfoundland has a more reliable association of scrub with forest, but a less reliable identification of bog than later updates to the forest inventory in the study area. Landsat imagery applications for forest inventory updates do not appear useful in identifying non-forest vegetation communities. Caution should be taken in using forest inventory maps in wildlife habitat applications when the habitat includes important non-forest components. Key words: forest inventory, habitat classification, Landsat imagery, mapping, remote sensing
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".