Can the Spatial Point Patterns of Animal Distributions Be Detected Using Sparse Samples? A Case Study of Four Soricomorpha (Mammalia) Species in Poland / Czy Przestrzenny Wzorzec Rozkładu Punktów W Dystrybucji Zwierząt Może Zostać Określony Na Podstawie Rzadkiego Próbkowania? Studium Przypadku Na Czterech Gatunkach Soricomorpha (Mammalia) Występujących W Polsce.
Bibliographic record
Abstract
Abstract In the present study, Riley's K function and alternative spatial point process models are calculated and compared for the hybrid distributional records of four Soricomorpha species (Talpa europaea, Sorex araneus, Sorex minutus, and Neomys fodiens) in Poland over different sampling sizes. The following spatial point process models are fitted and compared: homogeneous Poisson process (HPP) and inhomogeneous Poisson process (IPP) models. For IPP models, the covariates explaining the trend are latitude and longitude. Spatial process models and true distributional aggregation status (using K function) of the four species are also calculated based on the full observed data set for the purpose to check how many grids are required to sample so as to reflect the true spatial distributional point patterns. When performind tha sampling, the sanpling size 5, 10, 30, 60 and 100 are considered. For each sampling size, 500 replicates are performed to keep consistence and reduce uncertainty. The results showed that, for the full observed data set over the whole territory of Poland, IPP models were much better than the null HPP model for explaining the distribution of Soricomorpha species. For every sample size, the true aggregation status and the associated spatial point process models of each species over the studied area can be perfectly identified when using the information derived from limiting samples only. Based on the results, it is found that around 20% of grid cells should be used as the minimum threshold for accurately detecting the true spatial point patterns
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".