Trembling aspen response to a mixed-severity wildfire in the Black Hills, South Dakota, USA
Bibliographic record
Abstract
Trembling aspen (Populus tremuloides Michx.) regeneration dynamics including sprout production, growth, and clone size were measured to determine the effects of fire on small aspen clone persistence following a mixed-severity wildfire in the Black Hills, South Dakota. Four years postfire, 10 small, isolated aspen clones per low and high fire severity classes were compared with 10 unburned clones. Regardless of severity, fire did not cause an increase in the area occupied by individual aspen clones. Clones affected by high severity fire had the greatest suckering response producing an average of 31 930 sprouts·ha1; more than double the sprout density in unburned clones and 67% greater than the sprout density in clones affected by low severity fire. Sprout growth in high severity clones was 135% and 60% greater than sprout growth in unburned and low severity clones. The succession of these clones to more shade-tolerant ponderosa pine was delayed in clones affected by high severity fire as high severity fire caused significant pine mortality within and surrounding the clone, whereas, without further disturbance, pine encroachment and dominance will likely continue in clones affected by low severity fire.
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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 | 0.000 | 0.000 |
| 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".