Smartphones Reveal Angler Behavior: A Case Study of a Popular Mobile Fishing Application in Alberta, Canada
Bibliographic record
Abstract
Abstract Successfully managing fisheries and controlling the spread of invasive species depends on the ability to describe and predict angler behavior. However, finite resources restrict conventional survey approaches and tend to produce retrospective data that are limited in time or space and rely on intentions or attitudes rather than actual behavior. In this study, we used three years of angler data from a popular mobile fishing application in Alberta, Canada, to determine province-wide, seasonal patterns of (1) lake popularity that were consistent with conventional data and (2) anthropogenic lake connectivity that has not been widely described in North America. Our proof-of-concept analyses showed that mobile apps can be an inexpensive source of high-resolution, real-time data for managing fisheries and invasive species. We also identified key challenges that underscore the need for further research and development in this new frontier that combines big data with increased stakeholder interaction and cooperation. El manejo exitoso de las pesquerías y el control de la dispersión de especies invasivas depende de la habilidad para describir y predecir el comportamiento de los pescadores. Sin embargo, la limitación de recursos restringe el uso de muestreos convencionales y tiende a producir datos históricos incompletos en tiempo y espacio, y se fundamenta en intenciones o actitudes más que en el comportamiento real de los pescadores. En este trabajo se utilizan tres años de datos sobre pescadores obtenidos mediante una aplicación para teléfonos móviles en Alberta, Canadá, para determinar, a nivel provincie, los patrones estacionales de: 1) popularidad del lago de acuerdo a los datos convencionales, y 2) conectividad antropogénica del lago que no ha sido ampliamente descrita en Norteamérica. El análisis para poner a prueba el concepto mostró que las aplicaciones para teléfono celular pueden representar una fuente de datos barata, de alta resolución y que opera en tiempo real para manejo de pesquerías y de especies invasivas. También se identificaron retos clave que resaltan la necesidad de realizar investigación en el futuro y desarrollar información acerca de esta nueva frontera tecnológica que combina grandes cantidades de datos y mayor interés y cooperación por parte de los inversionistas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".