Recent Vegetation Change (1980–2013) in the Tundra Ecosystems of the Tuktoyaktuk Coastlands, NWT, Canada
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
Change detection studies using remote sensing and plot-based sampling show that Arctic vegetation is changing. Most studies have focused on the proliferation of tall shrubs, but increased productivity in areas where shrub cover is low suggests that other functional groups may also be changing. To investigate vegetation change across the Tuktoyaktuk Coastlands we analyzed high-resolution repeat air photos from 1980 and 2013. Thirty-eight image pairs were used to estimate changes in the cover of six functional groups (tall shrub, dwarf shrub, non-tussock-forming sedge, tussock-forming sedge, moss, and lichen). The spatial extent of our airphotos allowed us to investigate changes across four terrain types (high-center polygonal terrain, low-center polygonal terrain, shrub tundra, and tussock tundra). Our analysis shows that all four terrain types experienced absolute increases in shrub cover (+7.71% to 11.98%), with the expansion of dwarf shrubs playing an especially important role in regional change. Significant declines in lichen cover were also observed. While the consistency of shrub encroachment across terrain types suggests that changes were facilitated by shifts in broad-scale processes like temperature or precipitation, our data also indicate that differences in the magnitude of change were mediated by community structure and the availability of suitable microsites.
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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 | 0.000 |
| 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) | 0.001 | 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