An accumulation rate curve estimator for total species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper we present a total species estimator based on modelling the rate of change of a species accumulation curve (SAC). The proposed approach calculates an accumulation rate curve (ARC) for new species conditional on observed data and extrapolates it using parametric functions with varying rates of decay. The curve fits are integrated to obtain estimates for undetected species and a weighted estimate is calculated by optimizing a loss function subject to a set of restrictions. Confidence intervals are evaluated using a parametric bootstrap of aggregate counts, with the underlying count covariances estimated from a regularized mixture distribution fit to observed count data. A data smoothing technique and adjusting for bias are also discussed. The method is tested using a simulation study and applied to two example datasets. The results indicate that the proposed method is robust in a majority of cases and outperforms existing methods in bias and mean squared error. Performance is especially improved when the proportion of unobserved species is high. Confidence interval coverage is noticeably better compared to existing methods and conservative interval widths are maintained. The smoothing technique is also shown to be effective in reducing mean squared error under certain conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it