Estimating change in species richness from repeated sampling of incidence data
Bibliographic record
Abstract
Forest tree species richness is an important indicator of sustainability. Forest monitoring allows stake-holders to track species richness over time. To test a hypothesis of whether an observed change in richness is significant or not, the analyst must choose a suitable estimator. Based on experience from comparative studies of potential estimators this study evaluates the performance of one design-based and three model-based estimators of change in species richness. The evaluation is done with Monte Carlo simulations of simple random sampling from four actual populations of forest tree species incidence data collected at two occasions from a fixed set of fixed-area forest inventory plots. The observed change in species richness (design-based) had the lowest root mean squared error, but often more biased than estimates from the three model-based estimators. The bias issue and poor coverage of 95% confidence intervals dissuade the use of the design-based estimator (observed change). A newly developed urn-type estimator, easily adaptable to processing longitudinal data, was overall best in terms of bias and coverage.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".