Establishing a Baseline of Recent Grassland Variability along the Saskatchewan - Montana Border
Bibliographic record
Abstract
The northern Great Plains of North America are significant for three reasons: (i) They are the source for much of the food produced in North America; (ii) They encompass the last remaining native habitats of many endangered species; and (iii) Their vulnerability to climate change is second in North America only to the Arctic. Paleoclimate records for the northern Great Plains show prolonged droughts far more extreme than those that have been experienced since European settlement. There is concern that one of the most immediate impacts of global warming in this region will be a return to past conditions, putting tremendous strains on the sustainability of natural, physical and social prairie infrastructures. In this research we document the variability of prairie grassland environments along the Saskatchewan – Montana border in order to develop a deeper understanding of their spatial and temporal responses to recent climatic events. We applied temporal mixture analysis and principal components analysis to a thirty-year time series of Landsat imagery to identify significant spatial patterns and temporal signals of vegetation vigour. We compared these with concurrent climatic events to gain insights on their responses. We summarize our findings as a baseline of current conditions to which the significance of future changes can be measured.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".