Detecting changes in sub-arctic vegetation caused by Snow Goose foraging on Coats Island: A multi-temporal remote sensing analysis
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
Foraging of overabundant light geese in North America has caused detrimental impacts on vegetation surrounding their sub-arctic breeding grounds. Consequently, this increase in barren land cover removes nesting habitat for sympatric species. Coats Island, Nunavut is a unique study area where changes in vegetation can be investigated before/after light geese have first begun breeding on the island. The objectives of this thesis were to: (i) create an annual land cover classification of northern Coats Island (1991-2014) from LANDSAT imagery; and (ii) detect changes in vegetation on Coats Island potentially attributable to foraging light geese. Using LANDSAT 5 TM and LANDSAT 8 OLI satellite images and six ASTER GDEM tiles, a supervised classification with a random forest classifier was used to annually classify northern Coats Island (1991-2013). LANDSAT- and ASTER GDEM-derived variables were also used to improve the classification accuracy. Training areas for six land cover types were created from ground truthing points (n=85) collected in 2014 along with visual interpretation of the imagery. Normalized Difference Vegetation Index (NDVI) surfaces were created from LANDSAT 5 TM and LANDSAT 7 ETM+ imagery to assess changes in vegetation quality. My results correspond with on-site field observations which suggest that light geese have had no major, negative impact on local vegetation on Coats Island, in contrast to vegetation damage observed at other similar coastal habitat sites with nesting light geese colonies around Hudson Bay.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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