VARIABILITY OF FISH PRODUCTION: NUTRIENTS AS CHEMICAL DRIVERS ACROSS A DIVERSE GEOGRAPHIC RANGE
Bibliographic record
Abstract
Total phosphorus (TP) is an essential nutrient established as a driver of primary productivity that we predict plays a large role in the fish biomass in rivers, lakes and reservoirs.Watershed catchment geology and anthropogenic modification to river flow including hydropower influence nutrient variability in rivers.The relationship between nutrient variation and fish biomass was compared on a local-scale between two geological distinct mountain watersheds in southeastern BC, providing an opportunity to examine smaller-scale ecosystem response to nutrient availability.Nutrient regimes were also characterised in rivers regulated by hydropower and reference rivers across Canada to assess water quality trends and expected variability in fish biomass across a broad geographic scale.Local scale aquatic assessment is useful for identifying trends in ecosystem response within a watershed, whereas regional assessments are at a scale that is more applicable for management.Using TP and fish biomass relationships from a meta-analysis of literature, we developed regionally specific nutrient-based fish models.Establishing baseline nutrient regimes and developing models to estimate expected fish biomass specific to regional fish diversity, provides a useful predictive tool for initiating mitigation and compensation for rivers affected by hydropower.Hontela for their support and feedback on my project over the last two years, and to Dr. Mike Bradford my external examiner.This project would not have been possible without funding provided by NSERC HydroNet, University of Lethbridge, and the Industrial support from Lotic Environmental.I would like to thank Jesse Malkin for his enthusiastic attitude and strong back, helping me haul nets and electrofishers through mountain rivers in the East Kootenays.Thank you also to all the members of the Young Researchers Committee across Canada who not only assisted in my data collection but also introduced me to and interconnected so many interesting research topics.Thank you to Atle and CEDREN (Center for Environmental Design and Renewable Energy) in Norway, for the opportunity to gain international perspective on approaches to ecosystem management, and to taste some very expensive Norse delicacies.I truly appreciated being part of HydroNet, a research collaboration that provided great perspective to understanding the process and knowledge transfer from field research to applied management.Thank you to the members of my lab; I could not have had a more supportive group of friends to bounce ideas off of.I look forward to watching our experiences and professional lives intertwine in the future.I am also very grateful to have such supportive friends and family and I intend to spend more time with all of you!Finally, thanks to the best angler I know and my partner Trevor, for helping me keep my experiences in perspective.You kept the stove burning and the garden growing, I could not have done this without you.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".