Benthic Data Collection in Lake Ontario: Continuation of a Long‐term Data Series
Bibliographic record
Abstract
Photo 1. Marine technician Kathryn Johncock is collecting a benthic sample from Lake Ontario using Ponar bottom grab aboard of U.S. EPA Research Vessel Lake Guardian during 2018 benthic survey. Photo credit: A. Karatayev. Photo 2. Benthic samples are elutriated aboard and washed through a 500-μm net by Chris Korleski (U.S. EPA Great Lakes National Program Office, on left) and Alexander Karatayev (SUNY Buffalo State, on right). Photo credit: L. Burlakova. Photo 3. Exotic byssate bivalves quagga mussels were found in almost every benthic sample collected from Lake Ontario in 2018. Photo credit: A. Karatayev. Photo 4. Lake Ontario shoreline scattered with wind turbines. Photo credit: L. Burlakova. Photo 5. Lake Guardian is moving to the next station. Photo credit: L. Burlakova. These photographs illustrate the article “Density data for Lake Ontario benthic invertebrate assemblages from 1964 to 2018” by Burlakova, L. E., A. Y. Karatayev, A. R. Hrycik, S. E. Daniel, K. Mehler, L. G. Rudstam, J. M. Watkins, R. Dermott, J. Scharold, A. K. Elgin, T. F. Nalepa, E. K. Hinchey, and S. J. Lozano published in Ecology. https://doi.org/10.1002/ecy.3528.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.066 | 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 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".