Climatic limiting factors of North American ecosystems: a remote-sensing based vulnerability analysis
Bibliographic record
Abstract
Abstract Remote-sensing based vulnerability assessments to climate change are a research priority of critical importance for landscape-scale efforts to prioritize conservation and management of ecosystems. Limiting climatic factors can serve as a proxy for quantifying ecosystem vulnerability, since theory predicts that ecosystems close to critical climate thresholds will be more sensitive to interannual variation in limiting climate factors. Here, we analyze time series of enhanced vegetation index data for continental-scale vulnerability assessments. The analytical approach is a lagged monthly correlation analysis that accounts for memory effects from the previous growing season. Mapping multivariate correlation coefficients reveals that drought vulnerabilities can be found across the continent, including a distinct geographic band across the western boreal forest. The analytical approach reveals climate dependencies at high spatial and temporal resolution, with the direction and strength of correlation coefficients indicating the risk of threshold transgressions at the edge of species and ecosystem tolerance limits. The approach is further useful for hypothesis testing of contributing non-climatic factors to climatic vulnerability, allowing locally targeted management interventions to address climate change.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".