An Integrated Framework for Estimating the Number of Classes with Application for Species Estimation
Bibliographic record
Abstract
The two most common approaches for estimating the number of distinct classes within a population are either to use sampling data directly with combinatorial arguments or to extrapolate historical discovery data.However, in the former case, such detailed sampling data is often unavailable, while the latter approach makes assumptions on the form of parametric curves used to fit the discovery data, that are often lacking in theoretical justification.Instead, we propose an integrated transdisciplinary framework that dissolves the boundaries between the above two approaches.This is achieved by directly describing the samplingdiscovery process in parallel with describing a co-variate latent e↵ort process, where we have historical discovery data for the former process and some proxy data for the latent process.The linkage between these two processes allows one to form data on sampling records by forcing some constraints on how many samples were taken over time.Due to the nature of the constrained data, many inference techniques become infeasible.However, simulation-based methods such as Approximate Bayesian Computation remain available.Our proposed approach is demonstrated and analysed through many simulation experiments, and finally applied in the ecology field to estimate the number of species as an example of the number of classes problem.Doing my PhD research has been a unique and rewarding journey.I am grateful to all the circumstances that led me to this intellectual point in my life that reflected on my academic knowledge and personality.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".