Vegetation Succession and Environmental Conditions following Catastrophic Lake Drainage in Old Crow Flats, Yukon
Bibliographic record
Abstract
Increases in the frequency and magnitude of disturbances associated with the thawing of ice-rich permafrost highlight the need to understand long-term vegetation succession in permafrost environments. This study uses field sampling and remote sensing to explore vegetation development and soil conditions following catastrophic lake drainage in Old Crow Flats (OCF). The data presented show that vegetation on drained lake basins in OCF is characterized by two distinct assemblages: tall willow stands and sedge swards. Field sampling indicates that these alternative successional trajectories result from variation in soil moisture following drainage. Increased willow mortality on older drained basins suggests that intraspecific competition drives self-thinning in shrub thickets. This finding, combined with data from paleoecological studies and contemporary vegetation in OCF, suggests that willow stands on drained lake basins are seral communities. These results also indicate that the increase in number of catastrophic drainages that occurred between 1972 and 2010 will alter regional vegetation in ways that affect wildlife habitat, permafrost conditions, and local hydrology.
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 | 0.001 |
| 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 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".