Vegetation-Permafrost Relations within the Forest-Tundra Ecotone near Old Crow, Northern Yukon, Canada
Bibliographic record
Abstract
Old Crow Flats is a 5600 km2 glaciolacustrine plain that straddles the forest-tundra ecotone in northern Yukon. Continuous taiga corridors occur in the entrenched river valleys, where annual mean ground temperatures (Tg) at the depth of zero annual amplitude at two locations were −3.1 and −4.0ºC in 2013. On the Flats, the vegetation cover is patchy, and Tg varied between −5.1 and −2.6ºC. Annual mean near-surface permafrost temperatures (Tps) measured on the Flats between 2008 and 2011 in patches of taiga, tall shrubs and low shrubs were correlated with local snow depth. Snow depth was controlled by vegetation height if the snow supply was not limited, for example, where low shrubs and large lakes dominate the landscape. In this setting, snow depths and, hence, Tps in taiga patches were higher (−2.6 to −2.9ºC) than in the surrounding shrub vegetation (−3.5 to −5.5ºC). Where taiga patches were more extensive, redistributed snow was trapped at the patch edge and Tps in taiga was lower (−4.1 to −4.3ºC) than in the surrounding shrub patches (−3.2 to −3.6ºC). The permafrost temperature field is heterogeneous under patchy vegetation in the forest-tundra ecotone. Our data suggest that it is governed by both the snow-holding capacity of local vegetation patches and the spatial configuration of vegetation in the surrounding landscape, which controls snow supply. Copyright © 2014 John Wiley & Sons, Ltd.
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 | 0.000 |
| 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 | 0.000 | 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".