Bibliographic record
Abstract
Global reliance on water resource development has resulted in the disconnection of key migratory pathways for numerous fish species, leading to population declines.Fishways represent one solution for reinstating connectivity, although their biological effectiveness often remains unknown and the mechanisms contributing to successful passage for most species is poorly understood.This thesis applied an interdisciplinary approach to investigate fishway passage by lake sturgeon.Identification of key spawning habitats downstream of a dam equipped with a fishway revealed that attraction and passage efficiency of the fishway unlikely limit reproductive success in this population of lake sturgeon; nevertheless, lake sturgeon still locate and pass the fishway annually.Overall fishway passage efficiency was 36% and successful passage was unrelated to adult sturgeon size or water temperatures.Successful passage events were highly variable in duration, and turning basins within the fishway considerably delayed passage and increased failure rates, leading to speculation that variability in energy use resulting from path selection may be a possible mechanism for delayed or failed passage.New methods were developed for the field quantification of sturgeon swimming activity and energy use using animal-borne accelerometers.Calibrations demonstrated the utility of accelerometers as a direct measure of volitional swimming speed and identified that sturgeon are capable of swimming at speeds in excess of those previously observed.Field application identified that sturgeon rarely used high speed swimming to traverse the fishway and that energy use was not predictive of successful passage, although successful individuals exhibited a higher cost of transport.Successful fishway passage resulted in an energetic cost equivalent to individuals travelling 2.1-13.3km in a lentic system.Other Completing this thesis (and not only surviving, but immensely enjoying my time in Canada) wouldn't have been possible without the support and friendship of a lot of people.My supervisor, Steve Cooke, was extremely supportive right from our first meeting in New Zealand prior to my move to Canada.Throughout my PhD his enthusiasm for fish and research has been an inspiration, and he has provided me with endless opportunities well beyond my expectations that I will always appreciate.Jeff Dawson co-supervised me and I'm grateful for his patience with my never-ending questions and for giving me his time to continually bounce ideas around with him.Pierre Dumont and Daniel Hatin from the Ministère des Ressources naturelles et de la Faune provided a huge amount of resources for this project in terms of their time, staff and equipment which made the project possible.Steve Perry and Chris Katopodis served on my committee and provided valuable support and guidance throughout my PhD.The entire Cooke Lab, past and present, were a continual source of friendship and helped to make me feel a part of one of the best workplaces I could imagine.The friendships I formed here also played a significant part in my time outside of work, providing awelcome distraction, whether it was simply sitting in a pub watching hockey, going camping, fishing or hunting, or inviting me to their family home during holidays as mine was so far away.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".