Variation in fire scar phenology from mixed conifer trees in the Sierra Nevada
Bibliographic record
Abstract
Fire scar based studies have provided robust reconstructions of past fire regimes. The season in which a fire occurs can have considerable impacts to ecosystems but inference on seasonality from fire scars is relatively uncertain. This study examined patterns in the phenology of cambium formation and wounding responses in the five common mixed conifer tree species of the Sierra Nevada. The outer bark was shaved on 35 trees and individual locations within the shaved portions were wounded systematically by applying direct heat using a handheld torch. Most of the trees had not commenced annual ring development by the first burning treatment in late May. By the second treatment, scars were identified mostly within the early or middle earlywood, although variation was high compared with other treatment periods. By late October, all scars were recorded at the ring boundary. Although intra-ring scar positions generally followed a logical temporal pattern, there was high tree to tree variation such as Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) burned on 26 June induced scars in the early, mid, and late earlywood depending on the individual tree. This high variation makes it somewhat challenging to precisely assign past fire season to published fire history studies.
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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 | 0.001 |
| 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 | 0.000 | 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".