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Record W1603185562 · doi:10.1080/03632415.2015.1049693

Smartphones Reveal Angler Behavior: A Case Study of a Popular Mobile Fishing Application in Alberta, Canada

2015· article· en· W1603185562 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFisheries · 2015
Typearticle
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsnot available
Fundersnot available
KeywordsFishingFisheryGeographyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.235
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it