Smartphones and Digital Tablets: Emerging Tools for Fisheries Professionals
Bibliographic record
Abstract
ABSTRACT Smartphones and digital tablets are used to collect data for agricultural, geographical, and medical research. Science professionals find these devices attractive because they contain many useful hardware accessories (e.g., camera, Global Positioning System [GPS], accelerometer) and the capacity to access and customize software applications (apps). To enhance student learning, some educators are also integrating tablets into curricula for both indoor and outdoor course work. Recently, fisheries professionals have begun using these devices for data collection and public outreach and awareness. With new waterproofing technology, cases, and peripheral adapters, smartphones and digital tablets are continually becoming more relevant for data collection and education in fisheries. Here, we synthesize some of the available information on smartphone and tablet use for data collection and education and explore some current uses and future opportunities for these devices in fisheries. Overall, our objective is to demonstrate that smartphones and digital tablets are useful tools for fisheries professionals, including technicians, managers, and educators. RESUMEN Los teléfonos inteligentes y las tabletas digitales se utilizan para colectar datos geográficos, de agricultura y de investigaciones médicas. Los profesionales de la ciencia encuentran atractivos estos dispositivos porque contienen accesorios útiles de hardware (p.e. cámaras, sistemas de posicionamiento geográfico –GPS-, acelerómetros, etc.) y además son capaces de brindar acceso y configurar aplicaciones de software (apps). Con el fin de mejorar el aprendizaje de los estudiantes, algunos educadores están integrando las tabletas digitales en las matrículas tanto dentro como fuera de los salones de clases. Recientemente, los profesionales de las pesquerías han comenzado a usar estos dispositivos para colectar datos, para difusión y concientización. Con nueva tecnología submarina, cubiertas y adaptadores periféricos, los teléfonos inteligentes y las tabletas digitales están volviéndose cada vez más relevantes para educación y para colectar datos pesqueros. En este estudio se resume parte de la información disponible en lo tocante al uso de teléfonos inteligentes y tabletas digitales con fines educativos y de recolecta de datos. También se exploran algunos usos actuales y oportunidades futuras que guardan estos dispositivos para la ciencia pesquera. El principal objetivo es demostrar que los teléfonos inteligentes y las tabletas digitales son herramientas útiles para los profesionales de las pesquerías, incluyendo técnicos, manejadores y educadores.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".