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Record W2197560679 · doi:10.1080/03632415.2015.1082472

Action Cameras: Bringing Aquatic and Fisheries Research into View

2015· article· en· W2197560679 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFisheries · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsCarleton University
FundersNational Institute of Food and AgricultureNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsNew York State Department of Environmental ConservationU.S. Department of Agriculture
KeywordsFisheryAction (physics)GeographyBiology

Abstract

fetched live from OpenAlex

Abstract Digital action cameras (ACs) are increasingly being utilized for aquatic research purposes due to their cost effectiveness, versatility, high-resolution imagery, and durability. Here we review the advantages of AC technology in research, with particular emphases on (a) research videography (both in the field and the laboratory), (b) animal-borne studies, and (c) outreach and education purposes. We also review some of the limitations of this technology as represented by environmental factors (e.g., depth, turbidity) and deployment considerations (e.g., lens choices, imaging settings, battery life). As AC technologies evolve in response to growing public interest in their application versatility, researchers are indirectly reaping the rewards, with technological advances that are innovative, cost-effective, and can withstand frequent use in dynamic and rugged field conditions. With such a diversity of options available, future usefulness of ACs in research will only be limited by the creativity of the scientists using them. Las cámaras digitales de acción (CA) están siendo cada vez más utilizadas con fines de investigación acuática debido a la efectividad en términos de costos, versatilidad, imágenes de alta resolución y durabilidad. Aquí se hace una revisión de las ventajas de la tecnología de las cámaras de acción en la investigación, con énfasis en (a) investigación videográfica (en campo y laboratorio), (b) estudios con animales y (c) propósitos de difusión y educación. También se revisan algunas de las limitaciones de esta tecnología en función de factores ambientales (p.e. profundidad y turbidez) y de consideraciones de uso (p.e. elección de lentes, opciones de imágenes, vida de las baterías). Dado que la tecnología de las CA evoluciona de acuerdo al interés del público en cuanto a la versatilidad de su aplicación, los investigadores están indirectamente cosechando los beneficios con avances tecnológicos que son innovadores, económicos y que pueden soportar el uso constante de las cámaras bajo las arduas condiciones del trabajo en campo. Con tal diversidad de opiniones disponibles, la utilidad de las cámaras de acción en el futuro, dentro del área de la investigación, sólo estará limitada por la creatividad de los investigadores que las usan. Les caméscopes sportifs numériques (CSN) sont de plus en plus utilisés à des fins de recherche aquatique en raison de leur rapport coût-efficacité, de leur polyvalence, de leurs images haute résolution, et de leur durabilité. Ici, nous passons en revue les avantages de la technologie du caméscope sportif en matière de recherche, en mettant l'accent sur (a) la vidéographie de recherche (à la fois sur le terrain et en laboratoire), (b) les études animalières, et (c) la sensibilisation et l'éducation. Nous examinons aussi certaines des limites de cette technologie telles que représentées par des facteurs environnementaux (par exemple, la profondeur, la turbidité) et les considérations d'utilisation (par exemple, le choix de lentilles, les paramètres d'imagerie, la durée de vie de la batterie). Les technologies CSN évoluent en réponse à l'intérêt croissant du public pour leur polyvalence. Les chercheurs en récoltent indirectement les fruits. Les progrès technologiques sont novateurs, rentables, et l'appareil résiste à un usage fréquent dans des conditions dynamiques et difficiles sur le terrain. Avec une telle diversité d'options disponibles, l'utilité future des caméscopes sportifs en matière de recherche ne sera limitée que par la créativité des scientifiques qui les utilisent.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.605
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.205
GPT teacher head0.359
Teacher spread0.155 · 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