The Effect of External Tag Variation on Profile Drag of Fusiform Teleost Fish
Bibliographic record
Abstract
The use of external active and passive tags to study locomotion, behaviour, and survival of fish has been common practice for decades.However, if tags significantly impact the organism's routine behaviour, tagging data may not accurately represent the general, untagged population.This study aimed to identify the effects of different external tags on profile drag (as a proxy for cost of transport) of fusiform teleost fish and how different tag attributes (tag size, antenna length, tag shape) affect drag.Chapter 2 used a rigid fish model with an embedded load cell in a water tunnel to compare drag added by tags attached to a fish ("tag drag") across a range of test flow velocities (0.17-0.37 m•s -1 ).Tag drag increased with velocity, but tag shape played a greater role in tag drag magnitude than tag size.Chapter 3 utilized the same apparatus in a factorial study comparing the effect of tag size, shape, and attachment location.The results demonstrated that large, cylindrical tags located anteriorly increase drag more than if they are moved posteriorly, and that small, tapered shaped tags placed at locations other than at the base of the dorsal fin are the least drag-inducing.From these results, it is recommended that tags should be as small as possible and flat; ideally tapered or streamlined rather than protruding and geometric.Tags also should not be placed at the base of the dorsal fin if they are not able to be close to the body wall.Dawson for their support, expertise, and patience throughout the past two years.Despite some disruptions along the way to the completion of this thesis, their unwavering confidence in my abilities inspired me to persevere.It has been such an incredible learning experience for me, having come from a background somewhat lacking in ecology and conservation, and has given me a new appreciation of fish ecology and fisheries science.Through the Fish Ecology and Physiology Lab (FECPL) at Carleton, I have had the privilege of meeting and working with some of the most knowledgeable and wellversed in the field of fish ecology.If not for the FECPL, I would never have had opportunities to attend conferences, volunteer for community outreach events, and tag bass for the first time.Thank you all!From the Department of Biology, I would like to thank Dr. Ryan Chlebak, Sabrina Phoenix, and Mike Jutting for their technical assistance and expertise.I would also like to thank David Raude and Dr. Daniel Feszty and the Department of Mechanical and Aerospace Engineering at Carleton for access to (and use of) their water tunnel.Of course, none of this would have been possible without the Ottawa-Carleton Institute of Biology and the wonderful folks at the Carleton Graduate Biology office
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".