Permafrost Based on Changes in Type and Density of Surface Vegetation
Bibliographic record
Abstract
This project will use satellite datasets in order to highlight alterations to permafrost based on changes in type and density of surface vegetation. Permafrost thaws due to climate change is a lesser studied phenomenon that has effects well beyond the Arctic ecosystems where permafrost exists. Permafrost thaw destabilizes landscapes which results in damage to man-mad infrastructure and leads to erosion of landscapes. The bigger concern, and one that has global implications, is that these frozen areas contain a significant amount of stored carbon. As these areas melt, organic matter that has been trapped in the frozen ground begins to release carbon dioxide and other greenhouse gases.This study will utilize satellite data from multiple sources to evaluate vegetation at several points in time. Data from the mid 1980’s will be acquired from Landsat 5 with more recent imagery acquired from Landsat 8. Spectral information contained within the data will be utilized to differentiate and quantify vegetation types. Ground truthing classification of the data will be done primarily through use of higher resolution satellite data (Pleaides-1) and ground photos taken during a summer field class in the summer of 2019.The study area is located in and around Churchill, Manitoba, Canada which is made up of three distinct eco-zones: Boreal Forest, Arctic Marine, and Arctic Tundra.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".