BEHAVIOURAL STUDY OF LABRADOR RETRIEVER IN AQUATIC ENVIRONMENT
Bibliographic record
Abstract
ACKNOWLEDGEMENTS First of all, I would like to thank my supervisor and co-supervisor, Prof. Doutora Liliana de Sousa and Prof. Doutora Ana Magalhães, respectively. They have guided and helped me through this dissertation and they perfectly complement each other. It’s been an extreme privilege to work with them and an inspiration to me. I’m extremely grateful for all the knowledge that they have shared with me and all the strength that they have given me during this process that is, eventually, an emotional one. I would also like to thank the professors that, during my first cycle degree in aquatic sciences and this master, had made me grow as an academic and as a person. Especially, I would like to thank to Prof. Doutor Eduardo Rocha, for believing in my capabilities, allowing me to execute this less conventional work and being always available when I needed some advice. A very sincere thanks to Sebastião Castro Lemos, proprietor of Quinta do Côvo, for his kindness in providing his farm to perform the research work. I would also like to thank him, the association Ânimas and the owners of domestic Labrador Retrievers for the use of
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".