Temporal changes in the potential geographic distribution of Histiotus velatus (Chiroptera, Vespertilionidae), the “decade effect”
Bibliographic record
Abstract
Also investigate how the potential distribution of this species changes with the addition of new records over the decades (decade effect). Assuming that (1: hypothesis of the effect of the decade) the addition of new occurrence records over time increases the potential size of the species distribution; and (2: Wallacean distance hypothesis) over the years, the new points added are increasingly distant from the research centers. Considering the geographic knowledge gap of Histiotu velatus, our objective is to report a new record of this species and estimate its potential distribution in South America through ENMs. For this, we compiled records of occurrence of species, selected from 1900 to 2015. We used 19 bioclimatic variables available in the WorldCLim database to estimate the potential distribution of the species and we used three modeling algorithms: Maximum Entropy (MXT) Random Forest (RDF) and Support Vector Machine (SVM). We selected the main bat research centers in Brazil, using the Lattes platform for the Wallacean distance hypothesis, using the Euclidean distance calculation. To test the hypothesis of the decade effect, we used beta regression analysis, taking conservative and non-conservative approaches. The results showed that the predicted area expanded and retracted over the decades, with an improvement in the accuracy of the models with the addition of new data. Most of the records are located in the southeastern region of Brazil, but the algorithms predicted areas in countries where there were no records. Only the conservatism approach has had a positive relationship over the decades. The distance from new points does not increase over the years of research centers.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".