Assessing landscape change in Waterton Lakes National Park, Canada, using multitemporal composites constructed from terrestrial repeat photographs
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 objective of this paper is to investigate landscape level changes that have occurred in Waterton Lakes National Park (WLNP), Canada between the years 1914 and 2005 using digital image processing techniques usually associated with satellite image analysis. Multitemporal colour composites, image classification, and principal components analysis were used to process registered images of the montane ecotone from photographic pairs of Bellevue Hill, Horseshoe Basin and Lakeview Ridge. The resulting digital images offered insight into the spatial nature of the vegetation changes that have occurred over the last 90 years at these sites. Changes observed included increased forest cover through vertical migration and the infill of conifers and aspen both on the slopes and the valley bottoms of WLNP.
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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) | 0.021 | 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