Bibliographic record
Abstract
PRBO Conservation Science (PRBO) conducted analyses and developed predictive models to identify areas that support aggregations of foraging seabirds (“hotspots”) and to inform Marine Spatial Planning (MSP) in the California Current System (CCS). We developed habitat associations for 16 species of seabirds using information from at-sea observations of individual species collected over an 11-year period (1997-2008). Environmental covariates reflected both spatial and temporal variation and included bathymetric variables, including proximity to oceanic habitat types (e.g., continental shelf and slope) and remote-sensed satellite data (sea-surface temperature, chlorophyll-a, sea-level height). At-sea surveys were conducted by numerous agencies and monitoring programs and extended from north of Vancouver Island to the US/Mexico border, extending out 600 km from the coast. We developed single-species predictive models using bagged decision trees, one type of machine-learning algorithm. Bathymetric variables, including proximity to land, were often important predictive variables. Oceanographic variables derived from remotely sensed data were generally less important. Model predictions were applied to the entire California Current for 4 months (February, May,
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.659 | 0.522 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".