Supplemental Results from: Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends
Bibliographic record
Abstract
These data contain the supplementary results corresponding with the journal article: Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends by Udell et al. (2024) in Ecological Monographs. These results contain the findings from the North American Bat Monitoring Program's (NABat) "Summer Abundance Status and Trends" analyses which used mobile transect acoustic data for three species (tricolored bat, little brown bat, and big brown bat). Data from the entire summer season (May 1–Aug 31) were used in the modeling process. Here, tabular data for each species include predictions (with uncertainty) of relative abundance (and trends over time) in the summer maternity season (May1 - July 16) from 2012-2020. Predictions for status and trends are provided for each species at four different spatial resolutions: 1) across the modeled species ranges, 2) at the state or province level, 3) at the NABat grid cell (10km x 10km scale) level, and 4) for each sampled transect. Predictions were produced using an analytical pipeline supported by web-based infrastructure, Bayesian hierarchical modeling, and 'false-positive N-mixture model' framework which analyzed mobile transect acoustics to model the relative abundance distribution (and trends over time) of each species while accounting for imperfect detection and species misclassification. Tabular files provided include: 1) range-wide average relative abundance predictions by year for each species, 2) range-wide trends in average relative abundance for each species, 3) regional (state/province) average relative abundance by year for each species, 4) regional (state/province) trends in average relative abundance for each species, 5) grid cell-level predictions of relative abundance by year for each species, 6) grid cell-level trends (overall change from 2012-2020) for each species (one file per species), and 7) transect-level estimates of relative abundance by year for each species. Estimates include means, medians, standard deviations, and the 95% Bayesian credible intervals. These data can be cross-referenced to the 'knitted' NABat master sample for CONUS, Canada, and Alaska (NABat_grid_covariates.shp, which is available on ScienceBase).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".