Tagging by Lobster Fishermen to Estimate Abundance of the Discarded Portions of Their Catch
Bibliographic record
Abstract
Abstract The cost of tagging studies can be greatly reduced if fishermen tag, release, and recapture during fishing operations. Some population components captured during fishing can not be legally retained. If these components are returned to the sea unharmed, their abundance can be estimated. Lobster tag, release, and recapture data were obtained by fishermen during their fishing activities over a 9-week season. Tags were inexpensive and easy to apply to cable ties. Abundance of ovigerous females and window females (non-ovigerous females, 114–124 mm carapace length) were estimated for seven and three fishing grounds respectively. When lobster fecundity was included in the calculations, annual egg production was also estimated. Fishery management applications include measuring the benefits of a regulation, and setting reference points to the numerical abundance of a life history stage. Experimental and area-specific management are more affordable if fishermen can collect data for stock assessment during their fishing operations. Data quality depends on carefully communicating the purpose and procedures to fishermen, and on designing a data sheet to reduce recording errors. Resumen el costo de los estudios de marcado puede reducirse sustancialmente si los pescadores mismos marcan, liberan y recapturan a los animales durante las operaciones de pesca. Algunos componentes poblacionales que son capturados durante las faenas no pueden ser retenidos legalmente. Si estos componentes se regresan intactos al mar, entonces es posible estimar su abundancia. Los datos de marcado, liberación y recaptura de langosta fueron tomados por los pescadores durante las actividades de pesca a lo largo de una temporada de nueve semanas. Las marcas fueron poco costosas y fáciles de aplicar. Se estimó la abundancia de hembras ovígeras y no ovígeras (éstas últimas con longitudes de carapacho de 114 mm a 124 mm) para siete y tres zonas de pesca, respectivamente. Cuando la fecundidad de las langostas fue incluida en los cálculos, también se estimó la producción anual de huevos. Las aplicaciones para el manejo pesquero incluyeron la medición de los beneficios de una regulación y el establecimiento de puntos de referencia para la abundancia numérica de los estadios de vida. El manejo experimental y por áreas es más costeable si los pescadores, durante las operaciones de pesca, pueden colectar datos útiles para la evaluación de los stocks. La calidad de los datos depende de que a los pescadores se les comunique cuidadosamente el propósito y procedimiento que implica el estudio así como del diseñ una hoja de captura que reduzca el error en los registros.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".