Modeling browse impacts on sapling and tree recruitment across forests in the northern United States
Bibliographic record
Abstract
Understanding the patterns of tree recruitment is essential to quantifying the future health and productivity of forest ecosystems. Using national forest inventory information, we incorporated browse impact measurements into models of sapling (2.5–12.7 cm diameter at breast height (DBH)) and overstory tree (≥12.7 cm DBH) ingrowth across the northern United States. Ingrowth was modeled with standard and zero-inflated techniques using discrete Poisson and negative binomial distributions. Zero-inflated models using stand attributes and browse impacts provided the best fit statistics for modeling the occurrence and frequency of ingrowth over a 5-year time period. Results indicate that stands with very high browse impact would contain 50.0% fewer ingrowth saplings compared with stands with no browse impact. Greater browse impacts similarly yielded a negative effect on overstory tree ingrowth, but to a lesser degree than saplings. Despite the stochastic nature of ingrowth observations, incorporating browse impacts may be essential in determining accurate levels of ingrowth in forests where herbivory constrains forest regeneration objectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".