Expert elicitation of state shifts and divergent sensitivities to climate warming across northern ecosystems
Bibliographic record
Abstract
Northern regions are warming faster than the rest of the globe. It is difficult to predict ecosystem responses to warming because the thermal sensitivity of their biophysical components varies. Here, we present an analysis of the authors’ expert judgment regarding the sensitivity of six ecosystem components – permafrost, peatlands, lakes, snowpack, vegetation, and endothermic vertebrates – across northern landscapes ranging from boreal to polar biomes. We identified 28 discontinuous component states across a 3700 km latitudinal gradient in northeastern North America and quantified sensitivity as the transition time from an initial to a contrasting state following a theoretical step change increase in mean annual air temperature of 5 °C. We infer that multiple interconnected state shifts are likely to occur within a narrow subarctic latitudinal band at timescales of 10 to more than 100 years, and response times decrease with latitude. Response times differ between components and across latitudes, which is likely to impair the integrity of ecosystems. Warming-induced changes in permafrost, peatlands, lakes, snowpack, vegetation, and vertebrates could impact northern ecosystem integrity, with different response times across components and latitude, according to an expert assessment of sensitivity in northern landscapes.
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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 | 0.000 | 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) | 0.000 | 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 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".