How promptly nonindustrial private forest landowners regenerate their lands after harvest: a duration analysis
Bibliographic record
Abstract
Understanding factors that influence how promptly landowners regenerate their timberlands after harvest, if at all, is critical to developing policies to improve forest productivity. Mississippi forest landowners with over 100 acres (1 acre = 0.404 ha) of forestland were surveyed in 2006 to collect harvest and regeneration data from 1996 to 2006. This study investigated the length of the time interval between harvest and reforestation. Nonparametric duration analysis was used to examine how long nonindustrial private forest landowners waited to reforest after harvesting. Parametric duration analysis was used to examine factors that influenced the length of this period. The mean time elapsed from harvest to regeneration was 11 months for landowners that regenerated their lands. The instantaneous probability of regeneration reached its highest value in the 16th month after harvest and, thereafter, decreased steadily until the 28th month, after which the probability of regeneration was essentially nil. Interest in timber production, employing a consultant, and ownerships that were predominantly pine forest types were factors associated with substantially shorter reforestation times. Lower stumpage prices and higher reforestation costs were associated with substantially longer reforestation times.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".