Bibliographic record
Abstract
Abstract In this paper we present an estimator for total species that is based on modelling an accumulation rate curve. The proposed approach calculates the curve for the rate of arrival of new species conditional on the observed data and projectes it forward using parametric functions with varying rates of decay. The individual fits are integrated to obtain estimates for undetected species and a weighted estimate is obtained by optimizing a loss function subject to a set of restrictions. Confidence intervals are obtained using a parametric bootstrap of aggregate counts, with the underlying count covariances estimated from a regularized mixture distribution fit to the observed count data. A technique to adjust the point estimate for bias is also discussed. The method is tested using a simulation study and two data examples. The results indicate that the proposed method is robust in a majority of cases and largely outperforms existing methods in bias and mean squared error. Performance is especially improved when the proportion of unobserved species is high. Confidence interval coverage probabilities are noticeably better compared to existing methods and conservative interval widths are maintained. The bias adjustment technique is also shown to be effective in reducing mean squared error.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".