Post-fire forest floor development along toposequences of white spruce - trembling aspen mixedwood communities in west-central Alberta
Bibliographic record
Abstract
After wildfire in the boreal forest, storage of organic carbon (C) begins with the accumulation of forest floor material. Soil properties of Gray Luvisols were studied to determine the differences in development along three toposequences. Our central hypothesis is that slope position does not influence the amount of accumulated organic C and total nitrogen (N) in the forest floor. Organic C and the C/N ratio in the forest floor and in A and B horizons increased from the crest to the toe of the slope. The forest floor contributed 2.0 ± 0.4 kg C·m2 (mean ± SE) at the crest to 3.5 ± 0.5 kg C·m2 at the toe. Throughout the solum, the C/N ratio was lower at the top of the slope compared with the toe (p < 0.05), and there were no differences among slope positions for in situ net N mineralization rates. Leaf area index, used as a proxy for net primary productivity, was greater (p < 0.05) at the toe compared with the crest position, and it was negatively correlated with forest floor total N concentration (r = 0.35, p = 0.027). These results, from mixedwood stands approximately 90 years after the last major fire disturbance, indicate that slope position does influence forest floor organic C by horizon volume (p = 0.02), but not total N concentration (p = 0.07). Despite the apparently lower N availability at the toe position, it exhibited the greatest potential productivity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".