USING DECOMPOSITION RATES TO INFER HOW FAR BACK TREE POPULATIONS CAN BE RECONSTRUCTED
Bibliographic record
Abstract
To study forest dynamics without relying on the space-for-time substitution, one must be able to follow a population or stand of trees back or forward in time. The method of stand reconstruction looks back in time by aging all the live trees and aging and dating the time of death of dead standing and fallen trees. However, dead trees are lost by decomposition so the record becomes increasingly incomplete with passage of time. Here we present a model of the passage of trees from dead standing to dead decomposed but still datable to completely decomposed and thus undatable or lost. We then generalize a method for calculating the falling rate of dead trees originally proposed in 1985 by A. P. Gore, E. A. Johnson, and H. P. Lo. We do this by removing the assumption that no trees are lost by decomposition, i.e., by using the decomposition rate. Finally, in the most important result, the model allows estimation of how far back a good estimate of the numbers in the population can be made if the decomposition rates are known.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".