Factors influencing thiamin concentrations in lake trout
Bibliographic record
Abstract
Firstly, I would like to thank my major advisor Dr. Jacques Rinchard, for his dedication, time and commitment to his students. He has pushed me both academically and intellectually to become a better version of myself. Next, I would like to thank the rest of my graduate committee Drs. Brian Lantry, Donald Tillitt, and Matthew Altenritter who contributed to the conceptualization and execution of this thesis. Special thanks to the dedicated professionals at the United States Geological Survey - Lake Ontario Biological Station including Dr. Brian Weidel and Scott Minikiem, Dr. Michael Connerton from the New York State Department of Environmental Conservation, all those who assisted with annual lake trout and prey fish surveys, and finally to the staff at the Allegheny National Fish Hatchery for providing lake trout on short notice. This work would not be possible without their hard work and perseverance. The undergraduate laboratory assistants in Dr. Rinchard’s lab, including Jarrod Ludwig and Lillian Denecke deserved recognition for their dependability and diligence in assisting with laboratory work. Finally, financial assistance was provided by the Brockport Distinguished Professor Award, the Department of Environmental Science and Ecology, and the Great Lakes Research Consortium. On a personal note, I would like to thank my friends Kylee Wilson, Kyle Morton and the rest of the Altenritter’s lab for providing substantial moral support, thoughtful conversations and fond memories that made my time at Brockport special. Lastly, I would like to thank my parents Kim and Chris Heisey for the sacrifices they ii made to make my educational journey a reality. Their love and support made this thesis possible and for that I have immense gratitude.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".