Bibliographic record
Abstract
Anthropocentric activities have led to unsustainable populations of various fish species around the world today. We have increased our cultivation rates to manage our own growing population sizes at the expense of fish species. As a result, fish stocks around the world are in decline and the fishing industry today is pushing them to the point of collapse. Although many people would like to believe that their grocery stores are stocking their shelves with fish from sustainable sources, that is not always the case, and the general population is lacking the knowledge to make informed choices when purchasing fish. We aim to assess the types of fish, their sources, and the information provided to consumers about the fish in grocery stores of the Queen’s student area. We will work closely with Food Basics, Metro, John’s Deli, and Loblaw’s. After assessing these stores we will inform the public on which grocery stores have the best practices, and also inform the stores on ways in which they can change to include information for consumers on the sources and methods of obtaining the fish sold in their stores. We would like to be able to provide the public with labels indicating where the fish was caught, how it was caught, whether it was farmed or fished, and whether it was sustainably sourced. We will implement a consistent format in all of the stores and raise awareness in Kingston about the issues facing the fishing industry and how our choices can impact fish species.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads 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".