Remote sensing techniques for determining landcover features: applications for a species at risk
Bibliographic record
Abstract
Remote sensing techniques are becoming more advanced and commonplace in conservation biology, and are used to study spatial patterns of various taxa. The main objective of this study was to determine whether supervised classification of landcover types within Landsat imagery could be accurately used to find or locate islands on lakes that may have been overlooked during ground transects in central Saskatchewan. Additionally, we used telemetry data from collared female caribou to determine which islands were used and in which season(s), and to determine island characteristics that make caribou more likely to select them. We were able to successfully identify all islands within bodies of water relevant to collared caribou using a supervised classification method, which suggests that our methods were adequate. We also determined that none of the island characteristics significantly influenced caribou selection according to an occupancy model, however females tended to choose islands with a higher vegetation cover (NDVI) during the summer months and a proportionally lower snow cover during the winter months, likely as forage and predator avoidance strategies respectively. Finally, we suggest directions for future studies as well as implications for both wildlife managers and land-use planners in Saskatchewan, Canada.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".