Assessing and modeling standing deadwood attributes under alternative silvicultural regimes in the Acadian Forest region of Maine, USA
Bibliographic record
Abstract
Estimating the amount of standing deadwood in forests is crucial for assessing wildlife habitat and determining carbon stocks. In this analysis, snags (standing dead trees) in various stages of decay were inventoried across eight silvicultural treatments in eastern spruce–fir forests in central Maine nearly 60 years after treatments were initiated. Several modeling strategies were developed to estimate number of snags per hectare in various stages of decay. An unmanaged reference area displayed the highest basal area and volume of snags (5.4 ± 3.1 m2·ha–1 and 29.4 ± 23.6 m3·ha–1, respectively, (mean ± standard deviation)), while the lowest basal area and volume (0.9 ± 1.0 m2·ha–1 and 3.1 ± 5.2 m3·ha–1) were observed in selection system with a 5-year cutting cycle. Models indicated that snag abundance was related to stand density, depth to water table, and the average harvest interval of the treatment. At a fixed stand density, approximately 140% more snags were predicted to occur in treatments with an average harvest interval of 55 compared with 5 years. An index of error reflecting the number of snags found in certain decay classes was reduced by 40% when predictions from count regression models fit with a mixed modeling strategy were used over ordinal regression. Results from these analyses can help to reduce the disparities between observed and modeled snag stocking levels and further our understanding of the relationships between live and standing dead trees inherent to eastern spruce–fir forests.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".