Characteristics of each landscape type, including region, mean elevation, mean annual air temperature (MAAT), air freezing degree days (FDD), air thawing degree days (TDD), and freezing <em>n</em>-factor (ratio of soil surface FDD to air FDD)
Bibliographic record
Abstract
<b>Table 1.</b> Characteristics of each landscape type, including region, mean elevation, mean annual air temperature (MAAT), air freezing degree days (FDD), air thawing degree days (TDD), and freezing <em>n</em>-factor (ratio of soil surface FDD to air FDD). The <em>n</em>-factor values are the least square means ±SE from a Tukey HSD post hoc test conducted after a significant effect of landscape type was found with a two-way ANOVA (<em>p</em> = 0.01); significant differences between means are denoted by different letters. <strong>Abstract</strong> Discontinuous permafrost in the North American boreal forest is strongly influenced by the effects of ecological succession on the accumulation of surface organic matter, making permafrost vulnerable to degradation resulting from fire disturbance. To assess factors affecting permafrost degradation after wildfire, we compared vegetation composition and soil properties between recently burned and unburned sites across three soil landscapes (rocky uplands, silty uplands, and sandy lowlands) situated within the Yukon Flats and Yukon-Tanana Uplands in interior Alaska. Mean annual air temperatures at our study sites from 2011 to 2012 were relatively cold (−5.5 ° C) and favorable to permafrost formation. Burning of mature evergreen forests with thick moss covers caused replacement by colonizing species in severely burned areas and recovery of pre-fire understory vegetation in moderately burned areas. Surface organic layer thickness strongly affected thermal regimes and thaw depths. On average, fire caused a five-fold decrease in mean surface organic layer thickness, a doubling of water storage in the active layer, a doubling of thaw depth, an increase in soil temperature at the surface (−0.6 to +2.1 ° C) and at 1 m depth (−1.7 to +0.4 ° C), and a two-fold increase in net soil heat input. Degradation of the upper permafrost occurred at all burned sites, but differences in soil texture and moisture among soil landscapes allowed permafrost to persist beneath the active layer in the silty uplands, whereas a talik of unknown depth developed in the rocky uplands and a thin talik developed in the sandy lowlands. A changing climate and fire regime would undoubtedly influence permafrost in the boreal forest, but the patterns of degradation or stabilization would vary considerably across the discontinuous permafrost zone due to differences in microclimate, successional patterns, and soil characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".