Canadian Healthy Oceans Network (CHONe): An Academic–Government Partnership to Develop Scientific Guidelines for Conservation and Sustainable Usage of Marine Biodiversity
Bibliographic record
Abstract
ABSTRACT The Canadian Healthy Oceans Network (CHONe) research program formed to unite leading academic and government researchers with managers from Canada's national resource agencies to address an urgent need for better scientific information on marine biodiversity in Canada's Atlantic, Pacific, and Arctic waters. Specifically, the network is producing diverse scientific products to inform policy commitments in conservation and sustainable use of marine biodiversity resources. A common disconnect between science-driven research and policy application results in a dearth of science information relevant to specific decisions that cannot wait for knowledge to accumulate. To narrow this gap, CHONe research structures around three interlinking and integrated themes of marine biodiversity, ecosystem function, and population connectivity. CHONe products span from baseline maps, databases, and barcodes as tools to understand processes and monitor future change; spatial and temporal predictive tools to maximize knowledge on biodiversity patterns; analytical and sampling tools to characterize and assess biodiversity and habitat relationships, decision-making frameworks for sustainable, integrated ocean management; new findings on biodiversity and ecosystem functioning relationships; to specific advice, data input, models, and frameworks for current ocean planning efforts. RESUMEN El programa de la Red Canadiense de Ecosistemas Saludables (RCES) fue concebido para unificar a líderes académicos e investigadores de gobierno con manejadores de recursos de las agencias federales del Canadá, con el fin de lograr un mejor entendimiento de la información científica concerniente a la biodiversidad marina en el Pacífico, Atlántico y Ártico canadienses. En específico, la red está generando diversos productos para informar los compromisos políticos en temas de conservación y uso sostenible de la biodiversidad marina. La desconexión entre la investigación científica dirigida y su aplicación en políticas públicas, resulta en una falta de información científica relevante para tomar decisiones cuya resolución no puede esperar a la acumulación de conocimientos. Para reducir esta brecha, la investigación llevada a cabo en la RCES se estructura en tres tópicos integrados y entrelazados: biodiversidad marina, funcionamiento de ecosistemas y conectividad entre poblaciones. Los productos derivados de la RCES van desde mapas de líneas base, bases de datos y códigos de barras como herramienta para comprender procesos y monitorear cambios en el futuro; herramientas predictivas para maximizar el conocimiento de los patrones espaciales y temporales de la diversidad, herramientas analíticas y de muestreo para caracterizar y evaluar la relación entre hábitat y biodiversidad; marcos conceptuales para la toma de decisiones en el contexto del manejo integral y sustentable del océano, nuevos hallazgos sobre biodiversidad y funcionamiento de ecosistemas; hasta la emisión de sugerencias específicas, suplemento de datos, modelos y sistemas de información para apoyar los esfuerzos de ordenación marina.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".