Data from: Rate of permafrost thaw and associated plant community dynamics in peatlands of northwestern Canada
Bibliographic record
Abstract
This dataset was collected to document the changing plant community, and associated environmental factors, as warming climate conditions accelerate permafrost thaw in northern peatland environments. Due to the insulative properties of dry, surface peat layers, discontinuous permafrost is preferentially found in peatlands, termed peat plateaux, where the volumetric expansion of ice-rich permafrost has resulted in a raised, dry ground surface dominated by lichens and, often, stunted black spruce forests. As ground temperatures warm, and the ice-rich permafrost thaws, the ground surface sinks to, or below, the water table, and these peat plateau environments change dramatically from black spruce and lichen-dominated peat plateaux to treeless moss- and sedge-dominated collapse scar environments. Data are from a set of 17 sites distributed along a latitudinal gradient in the Mackenzie Valley of Northwestern Canada. At each site, a transect of five to nine contiguous 1x1m quadrats was sampled, spanning the transition from peat plateau to collapse scar environments and, thus, capturing the zone of active permafrost thaw within peat plateaux as they transition to collapse scars. Fourteen of these sites were sampled at two time periods: 2007 and 2008 (T1: time 1), and 2017 and 2018 (T2: time 2) enabling an assessment of 10-year changes (9 years for one site). This dataset includes quadrat-level measurements of plant community composition (percent cover by species), frost depth, water table depth, peat depth, soil moisture, and canopy cover. Site level measurements consist of maximum peat depth, along with pH and electrical conductivity of collapse scar water samples, as well as the annual rate of lateral permafrost thaw. We also include basic site location parameters, as well as several climatic parameters, interpolated for each site using BioSIM software.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".