Engineering a solution to the problem of bycatch in the tuna fishery
Bibliographic record
Abstract
Most of our students enjoy seafood, and studies show that overall seafood consumption in the United States is rising (Agriculture and Agri-Food Canada 2012). Other research estimates that as much as 40% of the seafood caught worldwide is discarded, while countless sharks, whales, dolphins, birds, sea turtles, and other animals are unintentionally killed or injured by fishing gear (Kledjian et al. 2014). This capture of non-targeted species—known as bycatch—is a worldwide challenge to maintaining sustainable fisheries and protecting endangered species. In this activity, students use a simple classroom model to design and test solutions to bycatch in the tuna fishery. They collect and use data to optimize their design and consider how their model might look in real life. We tested the lessons in a high school marine science course, but it could be adapted to an environmental science or biology course from eighth grade up. After this lesson, students should be able to:
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".