A Census of Fishes and Everything They Eat: How the Census of Marine Life Advanced Fisheries Science
Bibliographic record
Abstract
ABSTRACT The Census of Marine Life was a 10-year, international research effort to explore poorly known ocean habitats and conduct large-scale experimentation with new technology. The goal of Census 2010 in its mission statement was to describe what did live in the oceans, what does live in the oceans, and what will live in the ocean. Many of the findings and techniques from census research may prove valuable in making a transition, which many governments have publicly endorsed, from single-species fisheries management to more holistic ecosystem management. Census researchers sampled continental margins, mid-Atlantic ridges, ocean floor vents and seeps, and abyssal plains and polar seas and organized massive amounts of past and new information in a public online database called the Ocean Biogeographic Information System (www.iobis.org). The census described and categorized seamount biology worldwide for its vulnerability to fishing, advanced large-scale animal tracking with acoustic arrays and satellite archival tags, and accelerated species identification, including nearshore, coral reef, and zooplankton sampling using genetic barcoding and pyrotag sequencing for microbes and helped to launch the exciting new field of marine environmental history. Above all, the census showed the value of investing in large-scale, collaborative projects and sharing results publicly. RESUMEN El Censo de la Vida Marina (Census, por su nombre en inglés) fue un esfuerzo internacional de investigación de diez años de duración diseñado para explorar habitats oceánicos poco conocidos y experimentar a gran escala con nueva tecnología. El objetivo de Census 2010, declarado en su misión, era describir “qué vive y que vivirá en los océanos”. Muchos de los hallazgos y técnicas generadas en Census pueden resultar valiosas para la transición de un manejo mono-específico a un manejo holístico, basado en el ecosistema; lo cual ha sido públicamente aprobado por muchos gobiernos. Los investigadores de Census muestrearon los márgenes continentales, las cordilleras oceánicas del Atlántico, ventilas hidrotermales del fondo marino, planicies abisales y mares polares; de igual forma organizaron cantidades formidables de información, tanto pasada como actual, en una base de datos pública y en línea llamada Sistema de Información de Biogeografía Oceánica. Census describe y categoriza la biología de los montes submarinos a nivel mundial para poder estimar su vulnerabilidad a la pesca; realiza marcado a gran escala de organismos utilizando técnicas avanzadas con arreglos acústicos y marcas satelitales; expide la identificación de especies, incluyendo muestreo costero de corales y zoo-plancton, basado en código genético de barras y secuencias de piro-mareaje para microbios; así mismo contribuyeron con el lanzamiento de la nueva disciplina “historia ambiental marina”. Pero sobre todo, Census mostró las recompensas que deja la inversión en proyectos colaborativos de gran escala y la presentación pública de los resultados.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".