Bibliographic record
Abstract
Auteur, naturaliste, homme d'affaires et avocat -ainsi que fonctionnaire -Moses Henry Perley était un personnage familier en Amérique du Nord britannique et au-delà, il y a un siècle et demi.Plus intéressant pour nous aujourd'hui, cependant, sont ses liens avec les pêcheries.En tant qu'observateur et journaliste, Perley a amoncelé des renseignements sur les pêcheries du dixneuvième siècle.Son rôle dans l'histoire de la pêche -en particulier les négociations pour régler les différends avec les États-Unis dans les années 1850 -est l 'objet de cet essai.An author, a naturalist, a businessman and a lawyer -as well as a public official -Moses Henry Perley was a familiar figure in British North America and beyond a century and a half ago.Of more interest to us today, however, are his connections with the fisheries.As an observer and reporter, Perley generated a wealth of information on the nineteenth-century fisheries.His role in the history of the fisheries is the subject of the following essay.Perley was born in New Brunswick in 1804, his family having been part of the great Yankee migration "down-east" that attended the end of the French and Indian wars.Close-knit and prolific, the Perleys were of ancient lineage in their native New England, and, throughout his career, drawn as he was to his heritage and his home, Moses worked diligently, in two societies American and British-American to advance the interests of New Brunswick and the rest of British America.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".