Revisiting resource selection probability functions and single‐visit methods: clarification and extensions
Bibliographic record
Abstract
Summary Models accounting for imperfect detection are important. Single‐visit (SV) methods have been proposed as an alternative to multiple‐visit methods to relax the assumption of closed population. Knape & Korner‐Nievergelt (Methods in Ecology and Evolution, 2015) showed that under certain models of probability of detection,SVmethods are statistically non‐identifiable leading to biased population estimates. There is a close relationship between estimation of the resource selection probability function (RSPF) using weighted distributions andSVmethods for occupancy and abundance estimation. We explain the precise mathematical conditions needed forRSPFestimation as stated in Lele & Keim (Ecology, 87, 2006, 3021). The identical conditions that remained unstated in our papers onSVmethodology are needed forSVmethodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of theRSPFor theSVmethodology. To complement the work by Knape and Korner‐Nievergelt, we study the performance of multiple‐visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function, multiple‐visit methods also lead to biased estimates. As a solution to this problem, we extend theSVmethodology to a class of models that allows use of scaled probability function. We propose a multinomial extension ofSVmethodology that can be used to check whether the detection function satisfies theRSPFcondition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated. We argue that the instances where theRSPFcondition is not satisfied are rare in practice. Hence, we disagree with the implication in Knape & Korner‐Nievergelt (Methods in Ecology and Evolution, 2015) that the need forRSPFcondition makesSVmethodology irrelevant in practice.
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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 | 0.030 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".