www.statassoc.or.th Contributed paper Inference Concerning the Conversion Efficiency for a Special Predator-prey System
Bibliographic record
Abstract
In this communication, we consider the inference problem for the ratio of two interaction parameters, the so-called conversion efficiency of the Lotka-Volterra ordinary differential equations system (ODEs). The stochastic model under consideration views the actual population sizes as random perturbations of the solutions to these ODEs. Namely, we assume that the perturbations follow correlated Ornstein-Uhlenbeck processes and thus, no assumption is made that the random variables are independent. In this context, we establish the uniformly most powerful unbiased test for the conversion efficiency parameter. The asymptotic properties of the proposed test are derived. A simulation study is conducted and this provided strong evidence that corroborates with the usual asymptotic theory of optimal tests. To illustrate the procedure, the proposed method is applied to the Canadian mink-muskrat data set. ______________________________
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.147 | 0.023 |
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".