Learnings from collaborative monitoring of remote wildlife populations: factors affecting changes in number and distribution of nesting Hudson Bay eiders ducks in the Belcher Islands
Bibliographic record
Abstract
Anthropogenic pressures are causing a global decline in biodiversity that, in turn, impacts the human communities depending on it.In the conservation effort, efficient management requires up-to-date and accurate information about the population dynamics, habitat requirements, and distribution of organisms.There is an increasing appreciation of the benefits of coproduction and the combination of multiple knowledge systems to increase our understanding of the rapidly changing ecosystems.In this thesis, I used data from a long-term collaborative monitoring program involving Inuit and federal government researchers to study the factors affecting changes in population size and nesting distribution of a harvested sea duck in south-eastern Hudson Bay, the common eider (Somateria mollissima).I also highlight practical challenges and propose solutions related to cultural and institutional barriers that impede the delivery of respectful approaches and best practices in collaborative research programs involving large and regulated institutions and remote Indigenous communities.I would first like to thank my two co-supervisors, Vivian Nguyen and Grant Gilchrist, who provided constant support, guidance, and thoughtful advice throughout this research project.I have learned immensely through numerous conversations with both of them.They helped me develop and constantly adapt my research project.Their constant optimism has helped me move forward and kept the process fun even during the toughest periods.Many thanks to Greg Robertson who provide clear and detailed answers to my numerous questions regarding survey design and statistical approaches.Greg really helped me push my scientific rigor and helped me better understand the application of some statistical methods to ecological questions.I also want to thank Joel Heath, Lucassie Arragutainaq, and Johnny Kudluarok who initiated the 2021-2022 survey project in the Belcher Islands and organized most of the fieldwork logistics in Sanikiluaq.It has been a pleasure to collaborate with them.Special thanks to Joel who continuously helped me navigate remote communication with Inuit collaborators in Sanikiluaq and who often played the intermediary to overcome language, cultural, and physical distance barriers.I'm thankful to the whole Sanikiluaq field crew for their devotion to rigorous data collection, the knowledge they shared with me about their territory, the good time spent on the land, the laughter, and all the country food shared under the tent or around the campfires.Thank you to Lisa Pirie-Dominix for securing the funding for the realization of the survey fieldwork and the gathering of the previous survey data.iv Many thanks to Holly Hennin who provided essential logistical support for the realization of the fieldwork and who consistently helped me navigate Environment Canada and Carleton University's various administrative procedures.Thank you to all my student colleagues who, despite the COVID-19 pandemic, strived to create a pleasant social dynamic within our lab.Particularly, thanks to Adam Perkovic and Allison Drake for numerous fun and enriching conversation about Arctic research.Thankfully to generous funding from
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".