Canadian Recreational Fisheries: 35 Years of Social, Biological, and Economic Dynamics from a National Survey
Bibliographic record
Abstract
ABSTRACT Fisheries and Oceans Canada has collected a unique, long-term data set on the social, biological, and economic dynamics of Canada's recreational fisheries. Starting in 1975, these data were collected through mail surveys to recreational anglers at 5-year intervals. A longitudinal analysis revealed that there was an average of 4.5 million licensed anglers catching an annual average of 255 million fish. Release rates were relatively high (53% of fish released on average), with recent survey data (2010) suggesting that release rates had exceeded 60%. Recreational anglers also contribute an average of $8.8 billion each year to the Canadian economy. However, recreational angling has become less popular over time, and the average age of participants has increased. The data were also useful for characterizing Canada's fisheries, including species-specific catch and harvest. Canada is one of the few countries to collect such extensive recreational fisheries data at a national scale and to do so at regular intervals, an approach that could be modeled by other countries. RESUMEN la agencia de Pesquerías y Océanos de Canadá ha recolectado una base de datos históricos de la dinámica social, biológica y ecológica de las pesquerías recreativas de Canadá. Esta información, que comienza en 1975, fue compilada a través de sondeos por correo postal, realizados a intervalos de cinco años, dirigidos a pescadores. Un análisis longitudinal reveló que existen en promedio 4.5 millones de pescadores con licencia, que capturan una media de 255 millones de peces. Las tasas de liberación fueron relativamente altas (53% de peces liberados) y los datos del sondeo más reciente (2010) indican que la tasa de liberación excede el 60%. Asimismo, los pescadores recreativos contribuyen, en promedio, con $8.8 mil millones anuales a la economía canadiense. Sin embargo, con el tiempo, la pesca recreativa se ha vuelto cada vez menos popular y el promedio de la edad de los participantes se ha incrementado. Los datos también fueron útiles para caracterizar las pesquerías de Canadá, incluyendo captura y cosecha por especie. Canadá es uno de los pocos países que recolectan datos de pesca recreativa de forma tan extensiva a nivel nacional y lo hace en intervalos regulares, algo que pudiera ser imitado por otros países.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".