Bibliographic record
Abstract
The FAO database for captures of marine species contains no fewer than 1,700 species (FAO 2020: 10). The Peruvian anchovy is the top one in terms of quantity; the captures were more than 7 million tonnes in 2018, even if this was just an average year for the anchovy (see Figure 2.6). Second comes the Alaska pollock, with catches of almost 4 million tonnes in 2018 (see Figure 2.2). At the other end there are many species with catches of less than a tonne. Most fish species consist of “stocks”, populations that are separated in space and with little or no interaction between them. This is the basic unit of analysis in fisheries science. Atlantic cod, for example, consists of several stocks with little or no interaction: Northeast Arctic cod, Baltic cod, Icelandic cod, North Sea cod, Northern cod of Newfoundland, and more. Some fisheries scientists think there may be subpopulations of these that could be identified as stocks in their own right. An economic analysis of fisheries must start with some basic facts about the productivity of nature. The most basic premises are that no fish produce no fish and that fish populations would not grow without limit if left untouched by humans. Furthermore, we know that some fish stocks have been exploited for thousands of years and yet not become extinct. It makes sense, therefore, to assume that a surplus growth will be generated if stocks are reduced below the upper limit set by nature. This surplus growth can sustain fishing for ever without endangering the continued existence of the fish population; the fishery would take only whatever the population does not need to maintain itself. Several factors may lie behind the phenomenon of surplus growth. Fishing changes the age composition of fish stocks towards younger, faster-growing cohorts. Fewer fish could mean less competition for a limited food supply (this phenomenon is likely to be stronger in confined lakes than in the openended ecosystems of the ocean). Older fish often eat younger individuals of the same species, so the survival of young cohorts could improve as older cohorts are depleted through fishing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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".