Recent Vegetation Change (1980–2013) in the Tundra Ecosystems of the Tuktoyaktuk Coastlands, NWT, Canada
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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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".