Multinomial logit estimation of a matrix growth model for tropical dry forests of eastern Bolivia
Bibliographic record
Abstract
Multinomial logistic (MNL) regression was employed to estimate the transition probabilities of a matrix growth model for dry forests of the eastern Bolivian lowlands. Probabilities of mortality, stability, and upgrowth of a size and species group were estimated as a function of tree and stand attributes influencing growth and mortality. Data for model estimation were drawn from logged and undisturbed permanent sample plots (PSPs) measured over 6 and 7 years in Chiquitania forests south of Concepción, Santa Cruz, Bolivia. The estimated transition probabilities of the MNL models are not significantly different from those derived from PSP data by the conventional approach of employing simple mean proportions of observed movements per guild and size class. MNL estimation is advantageous in that it generates a smoother distribution of transition probabilities across size classes, correcting for variance in the data and model estimation errors imposed by limited samples. Moreover, the MNL approach allows deterministic, stochastic, and dynamic prediction of forest evolution, while preserving the simple linear form of matrix models that facilitates their integration into economic optimization studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".