NSERC's HydroNet: A National Research Network to Promote Sustainable Hydropower and Healthy Aquatic Ecosystems
Bibliographic record
Abstract
Abstract NSERC's HydroNet is a collaborative national five-year research program initiated in 2010 involving academic, government, and industry partners. The overarching goal of HydroNet is to improve the understanding of the effects of hydropower operations on aquatic ecosystems, and to provide scientifically defensible and transparent tools to improve the decision-making process associated with hydropower operations. Multiple projects are imbedded under three themes: 1) Ecosystemic analysis of productive capacity offish habitats (PCFH) in rivers, 2) Mesoscale modelling of the productive capacity offish habitats in lakes and reservoirs, and 3) Predicting the entrainment risk of fish in hydropower reservoirs relative to power generation operations by combining behavioral ecology and hydraulic engineering. The knowledge generated by HydroNet is essential to balance the competing demands for limited water resources and to ensure that hydropower is sustainable, maintains healthy aquatic ecosystems and a vibrant Canadian economy. Resumen NSERC's HydroNet es un programa nacional colaborativo de investigación a cinco años que inició en el año 2010 e involucra a los sectores académico, gubernamental e industrial. El objetivo general de HydroNet es comprender los efectos que tienen las operaciones hidroeléctricas en los ecosistemas acuáticos y ofrecer herramientas científicas defendibles y transparentes tendientes a mejorar los procesos en la toma de decisiones que están asociados al uso de la energía hidroeléctrica. Diversos proyectos se encuentran insertos en tres grandes tópicos: 1) análisis ecosistémico de la capacidad productiva de los habitats para peces (CPHP) en ambientes fluviales, 2) Modelación de meso-escala de la capacidad productiva de los habitats para peces en lagos y embalses, y 3) predicción del riesgo de arrastre de peces hacia los embalses hidroeléctricos, en función del poder generador de las operaciones, combinando la ecología conductual y la ingeniería hidráulica. El conocimiento generado por HydroNet es fundamental para evaluar el balance entre la demanda por recursos hídricos limitados, para asegurar que la energía hidroeléctrica sea sustentable, que promueva la salud de los ecosistemas acuáticos así como también a la pujante economía canadiense.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".