Lake Erie Pelagic Prey Fish Community Data, 2022-2024
Bibliographic record
Abstract
Since 2005, the U.S. Geological Survey - Great Lakes Science Center's Lake Erie Biological Station (LEBS) has participated in a collaborative, multiagency effort to assess pelagic prey fish populations in the western (WB), central (CB), and eastern (EB) basins of Lake Erie through a combination of hydroacoustic and midwater trawl sampling. Assessing lake-wide distribution and abundance of pelagic prey species over time facilitates evaluation of large-scale drivers of fish production while also considering food-web dynamics at a smaller sub-basin scale. Information from this survey will support decision making within an ecosystem-based fishery management framework. In support of binational Great Lakes fishery management, the objectives of this survey are to provide density estimates of key pelagic prey species across Lake Erie, to assess spatial distributions of fishes, and annual fluctuations in abundance. A stratified-random sampling approach including 24 strata was carried out during July (Summer), starting in 2022. This data release adds 2024 data to the set for a total of 3 years using the standardized survey design and data processing workflow.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.024 |
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".