A candidate hypothesis about ecogenic science applied to fish and fisheries within the Great Laurentian Basin during the 19th and 20th Centuries
Bibliographic record
Abstract
Late in the 20th Century, participants in a trans-jurisdictional fisheries research network in the Great Laurentian Basin collaborated with participants of other research networks (waterfowl, piscivorous birds, benthic insects, plankton, bacteria, meteorology, hydrology, etc.) in a mega-scale happening during the years 1967 to 1992 that I call ‘The Great Laurentian Spring’. With a basin-wide version of adaptive management, the scientific researchers collaborated with citizen activists, private entrepreneurs, commission facilitators and governmental administrators in remediating harm done to the natural living features of the Great Laurentian Basin, particularly in the preceding 150 years. Like the degradation process that preceded it, the remediation process had features of a self-organizing movement that became complex beyond the ability of participants and observers to fully describe and explain it. Here I offer as an hypothesis, a rough sketch of how fisheries networkers in the Great Laurentian Basin came to play a role of helping to conserve valued fisheries and preserve vulnerable species during the degrading pre-Great Laurentian Spring period and then to help remediate harmful stresses, rehabilitate fisheries and prevent further degradation during the Great Laurentian Spring period and since then. In general fisheries researchers performed empirical science in responsible ways, with emphasis on the fish and on their habitats, and thus on the health of the aquatic ecosystems. Occasionally, the strongly modified natural system could be managed to produce major fisheries benefits, at least temporarily. The Scot T. Reid’s Common Sense science contributed to the American C.S. Peirce’s Pragmatism and together they informed the German A. Thienemann’s Limnology and the Canadians W.E. Ricker’s and F.E.J. Fry’s Fisheries Science. All along, mathematics of increasing sophistication played a role. Reputable criticisms of scientific inferences as well as untested and disreputable rhetoric of science deniers were taken seriously by the researchers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".