Exploring Landscape Change in Mountain Environments With the Mountain Legacy Online Image Analysis Toolkit
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
Since 1996, Mountain Legacy Project (MLP) researchers have been exploring change in Canada's mountain environments through the use of systematic repeat photography. With access to upwards of 120,000 systematic glass plate negatives from Canada's mountain west, the MLP field teams seek to stand where historic surveyors stood and accurately reshoot these images. The resulting image pairs are analyzed, catalogued, and made available for further research into landscape changes. In this article we suggest that repeat photography would fit well within the Future Earth research agenda. We go on to introduce the Image Analysis Toolkit (IAT), which provides interactive comparative image visualization and analytics for a wide variety of ecological, geological, fluvial, and human phenomena. The toolkit is based on insights from recent research on repeat photography and features the following: user-controlled ability to compare, overlay, classify, scale, fade, draw, and annotate images; production of comparative statistics on user-defined categories (eg percentage of ice cover change in each image pair); and different ways to visualize change in the image pairs. The examples presented here utilize MLP image pairs, but the toolkit is designed to be used by anyone with their own comparative images as well as those in the MLP collection. All images and software are under Creative Commons copyright and are open access for noncommercial use via the Mountain Legacy Explorer website. The IAT is at the beginning of its software life cycle and will continue to develop features required by those who use repeat photography to discover change in mountain environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it