Abundance‐ and incidence‐based estimation of total number of rare species in under‐sampled sites
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Questions Ecologists collecting field samples of biological data have a keen interest in addressing the following question: how many rare species are there in as‐yet unsurveyed additional samples? Depending on the size of a targeted additional sample, statistical models for estimating the number of rare species have not been systematically established and compared. Location Global. Methods For fairly comparing and predicting rare‐species richness at the same sample‐size baseline, we systematically developed and compared four estimators for rarefaction and extrapolation of rare‐species richness with a given specific abundance. These four estimators included a uniformly minimum variance unbiased (UMVUE), Bayesian‐weighted, Chao‐derived unweighted and naïve estimator. Results After extensive numerical tests, for conducting rarefaction of rare‐species richness (i.e., when additional sample size was not larger than the original one) it is recommended to implement UMVUE, as it has zero bias and coverage percentage closest to 0.95. However, the performance of Bayesian‐weighted and Chao‐derived estimators is also satisfactory. By contrast, for conducting extrapolation of rare‐species richness (i.e., when the additional sample size is larger than the original one), the Bayesian‐weighted estimator is recommended, as it has the best performance among the four estimators (here UMVUE is inapplicable). Conclusions There was no absolute winner, as the different estimators have their own merits and are recommended under different settings. When conducting rarefaction of rare‐species richness, UMVUE, which has the highest accuracy, is recommended. By contrast, when conducting extrapolation of rare‐species richness, the Bayesian‐weighted estimator is recommended, as it has the overall best performance. To facilitate the potential application in the comparison and prediction of rare‐species diversity using rarefaction and extrapolation techniques, an R package (fRSE) has been developed; it is freely distributed at the following URL: https://github.com/ecomol/fRSE .
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it