Attributed North American Grid-Based Offshore Sampling Frames
Bibliographic record
Abstract
This sampling frame is a set of grid-based, finite-area frames spanning the offshore areas surrounding Canada, the United States, and Mexico, and is intended for use with the North American Bat Monitoring Program (NABat). A Generalized Random-Tessellation Stratified (GRTS) Survey Design draw was added to the sample units from the raw sampling grids (https://doi.org/10.5066/P9XBOCVV). The GRTS survey design algorithm assigns a spatially balanced and randomized ordering (GRTS order) to each cell within its respective framework. Grid cells are prioritized numerically; the lower the number, the higher the sampling priority. Cells can then be selected for monitoring following the GRTS order, ensuring both randomization and spatial balance. Monitoring within this standardized framework allows statistical inference to non-surveyed locations and ensures the validity of analyses at regional and range-wide scales. NABat is a continental collaboration including state and provincial, federal, and local agencies intended to standardize the collection and storage of bat data. Alaskan and Canadian waters are combined into a single grid. There are 5 grids in total: Alaska/Canada (AKCAN) Offshore grid, Continental United States (CONUS) Offshore grid, Mexico Offshore grid, Hawaii Offshore Grid, and Caribbean Offshore grid. Grid boundaries are based on World Exclusive Economic Zone oceanic political boundaries and extend into the shoreline 30 kilometers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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".