Loss of community stability as a coherent ecological impact of a changing climate
Bibliographic record
Abstract
Climate models overwhelmingly show that 98% of the Earth experienced its highest level of warming during the twentieth century.During this period, a plethora of studies have demonstrated significant impacts on biota.The focus of my thesis is to study the stability of ecological communities to changes in climate, both observed and predicted.Climate Firstly, I would like to thank my advisors, Frédéric Guichard and Dominique Gravel.They both provided me with the freedom to develop my own ideas, but also the guidance to help them become fully fledged concepts.Without their help I would not have been able to grow into a well-rounded ecologist.Through their mentorship, I have been able to turn ecological problems into mathematical ones and back again.It has been my privilege to be their student and, in the future, my pleasure to work with them as a peer pushing ecological theory to new places.I would like to thank Peter Caines for his enthusiasm for taking his life's work in a new direction.He conferred on me his wisdom about hybrid dynamical systems in engineering broadening my perspective of the theory that became the foundation of my work on seasonality.I am extremely grateful to Pierre Legagneux and the group at the Centre d'études nordiques for providing me with the data that would allow me to construct the multi-season models for my last two chapters as well as their feedback which helped to shape them.I would also like to thank Catherine Potvin for taking a chance on me when I began my career in ecology.She provided me with a unique opportunity to apply my skills to understanding a daunting experiment to test biodiversity-ecosystem functioning.She introduced me to the idea of stability in ecology, starting with Pimm, and set the stage for what would become the underlying theme of my doctoral studies.Catherine Potvin,
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".