Bibliographic record
Abstract
Abstract Fisheries science emerged in the mid-19th century, when scientists volunteered to conduct conservation-related investigations of commercially important aquatic species for the governments of North Atlantic nations. Scientists also promoted oyster culture and fish hatcheries to sustain the aquatic harvests. Fisheries science fully professionalized with specialized graduate training in the 1920s. The earliest stage, involving inventory science, trawling surveys, and natural history studies continued to dominate into the 1930s within the European colonial diaspora. Meanwhile, scientists in Scandinavian countries, Britain, Germany, the United States, and Japan began developing quantitative fisheries science after 1900, incorporating hydrography, age-determination studies, and population dynamics. Norwegian biologist Johan Hjort’s 1914 finding, that the size of a large “year class” of juvenile fish is unrelated to the size of the spawning population, created the central foundation and conundrum of later fisheries science. By the 1920s, fisheries scientists in Europe and America were striving to develop a theory of fishing. They attempted to develop predictive models that incorporated statistical and quantitative analysis of past fishing success, as well as quantitative values reflecting a species’ population demographics, as a basis for predicting future catches and managing fisheries for sustainability. This research was supported by international scientific organizations such as the International Council for the Exploration of the Sea (ICES), the International Pacific Halibut Commission (IPHC), and the United Nations’ Food and Agriculture Organization (FAO). Both nationally and internationally, political entanglement was an inevitable feature of fisheries science. Beyond substituting their science for fishers’ traditional and practical knowledge, many postwar fisheries scientists also brought progressive ideals into fisheries management, advocating fishing for a maximum sustainable yield. This in turn made it possible for governments, economists, and even scientists, to use this nebulous target to project preferred social, political, and economic outcomes, while altogether discarding any practical conservation measures to rein in globalized postwar industrialized fishing. These ideals were also exported to nascent postwar fisheries science programs in developing Pacific and Indian Ocean nations and in Eastern Europe and Turkey. The vision of mid-century triumphalist science, that industrial fisheries could be scientifically managed like any other industrial enterprise, was thwarted by commercial fish stock collapses, beginning slowly in the 1950s and accelerating after 1970, including the massive northern cod crisis of the early 1990s. In the 1980s scientists, aided by more powerful computers, attempted multi-species models to understand the different impacts of a fishery on various species. Daniel Pauly led the way with multi-species models for tropical fisheries, where the need for such was most urgent, and pioneered the global database FishBase, using fishing data collected by the FAO and national bodies. In Canada the cod crisis inspired Ransom Myers to use large databases for fisheries analysis to show the role of overfishing in causing that crisis. After 1980 population ecologists also demonstrated the importance of life history data for understanding fish species’ responses to fishery-induced population and environmental change. With fishing continuing to shrink many global commercial stocks, scientists have demonstrated how different measures can manage fisheries for species with different life-history profiles. Aside from the need for effective scientific monitoring, the biggest ongoing challenges remain having politicians, governments, fisheries industry members, and other stakeholders commit to scientifically recommended long-term conservation measures.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".