A multivariate approach to vegetation mapping of Manitoba's Hudson Bay Lowlands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Hudson Bay Lowlands of Manitoba contain a wide range of vegetation types that reflect local variations in climate, geological history, permafrost, fire, wildlife grazing and human use. This study, in Wapusk National Park and the Cape Churchill Wildlife Management Area, uses a Landsat-5 TM image mosaic to examine landscape-level vegetation classes. Field data from 600 sites were first classified into 14 vegetation classes and three unvegetated classes. Principal component analysis was used to examine the spectral properties of these classes and identify outliers. Multiple discriminant analysis was then applied to determine the statistical significance of the vegetation classes in spectral space. Finally, redundancy analysis was used to determine the amount of vegetation variance explained by the spectral reflectance data. We advocate this adaptive learning approach to vegetation mapping, by which the researcher employs an iterative strategy to carefully examine the relationship between ground and spectral data. This approach is labour intensive, but has the advantage of producing vegetation classes that are spectrally separable, decreasing the likelihood of errors in classification caused by overlap between classes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it