Snag characteristics and dynamics following natural and artificially induced mortality in a managed loblolly pine forest
Bibliographic record
Abstract
A 14-year study of snag characteristics was established in 41- to 44-year-old loblolly pine (Pinus taeda L.) stands in southeastern USA. During the initial 5.5 years, no stand manipulation or unusually high-mortality events occurred. Afterwards, three treatments were applied consisting of trees thinned and removed, trees felled and not removed, and artificial creation of snags produced by girdling and herbicide injection. The thinned treatments were designed to maintain the same live canopy density as the snag-created treatment, disregarding snags that remained standing. We monitored snag height, diameter, density, volume, and bark percentage; the number of cavities was monitored in natural snags only. During the first 5.5 years, recruitment and loss rates were stable, resulting in a stable snag population. Large snags (≥25 cm diameter) were common, but subcanopy small snags (10 to <25 cm diameter) dominated numerically. Large natural snags survived (90% quantile) significantly longer (6.0–9.4 years) than smaller snags (4.4–6.9 years). Large artificial snags persisted the longest (11.8 years). Cavities in natural snags developed within 3 years following tree death. The mean number of cavities per snag was five times greater in large versus small snags and large snags were more likely to have multiple cavities, emphasizing the importance of mature pine stands for cavity-dependent wildlife species.
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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".