Bibliographic record
Abstract
My topic is the role of property rights in marine capture fisheries, but given the awareness in Tasmania of the importance of aquaculture I will start with some figures on the relative importance of these two sectors of the fishing industry. World annual marine and inland aquaculture production has been steadily increasing to around 40 million mt, whereas annual production from marine capture fisheries seems to have hit a plateau (for the present) at 80 million mt, with a further 10 million mt coming from capture fisheries in inland lakes. The statistics on production of capture fisheries refer to landings, rather than catches – they omit the further 30 million mt of discarded by-catch. Of the landings of capture fisheries about one-third is used as feed for aquaculture species (10 million mt) or farm animals (20 million mt). In other words, of the fish we eat directly, 40% is farmed and 60% comes from capture of wild fish. Of the wild fish we catch we eat half directly, use a quarter as feed in farming, and throw a quarter away. I now turn to consideration of the world’s marine capture fisheries.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".