Enhancement-classification and spectral mixture analysis of caribou lichen habitats, northern Quebec, Canada
Bibliographic record
Abstract
We present a new approach for the detection and mapping of lichen in a heterogeneous habitat of northern Quebec using Landsat imagery. Results from the enhancement-classification method (ECM) and from spectral mixture analysis (SMA) were compared and evaluated for their potential to be applied over large territories. ECM is based on guided unsupervised classification of enhanced satellite images, and provided an overall accuracy of 74.5% and a good discrimination between lichen and non-lichen habitat. However, ECM discrimination between lichen habitats was more problematic. SMA is based is based on the fact that each pixel contains different surface features, each of which contributes to the overall pixel-level signal received by a remote sensor. SMA quantifies the spectral contributions from individual scene components to retrieve their sub-pixel scale proportions. Image SMA fractions showed a good agreement with field validated fractions. Tho two methods were efficient for mapping lichen. However, SMA provided new information not available by classification methods and therefore is recommended for further application to larger areas and spatio-temporal studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".