Environmental controls on ground temperature and permafrost in Labrador, northeast Canada
Bibliographic record
Abstract
Abstract Field data from 83 environmental monitoring stations across Labrador, 17 with permafrost, were used to analyze the interrelationships of key variables considered in the temperature at the top of permafrost model. Snow depth, not mean annual air temperature, was the strongest climatic determinant of mean temperatures at the ground surface and at the base of the annual freeze–thaw layer, and its variability was most closely related to land cover class. A critical late‐winter snow depth of 70 cm or more was inferred to be sufficient to prevent the formation of permafrost at the monitoring sites, which meant that permafrost was absent beneath forest but present in some tundra, peatland and bedrock locations. Analyses showed no statistically significant relations identified between topographic indices and various station parameters, challenging their utility for regional modeling. Testing of several different land cover datasets for model parameterization gave errors in ground surface temperature ranging from ± 0.9 to 2.1°C. These results highlight the importance of local field data and emphasize the necessity of high‐quality national‐scale land cover datasets suitable for permafrost modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".