Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Between 1970 and 2012, marine wildlife populations have declined by 49% (World Wildlife Fund (WWF), 2015, p.6). Over 90% of the typical fish populations in the oceans are fully exploited, overexploited, or depleted (Kituyi, 2018). In Canada, less than one in three fish stocks (30.4%) can be considered healthy (Thorne, 2021). Nevertheless, the oceans play an integral part for all life on earth and are crucial for tackling the climate crisis. They absorb up to 90% of the excess heat resulting from climate change and play a vital role as a carbon sink by absorbing up to 23% of human emissions (World Bank, 2022). Additionally, they have a considerable social and economic importance; in Canada, marine activities like fishing, tourism and oil and gas extraction account for about 1.6% of Canada’s total employment and gross domestic product (GDP) (Ganter et al., 2021).
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.
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 | 0.001 |
| 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 it