Using Species Distribution Models to Predict Suitable Habitat for Threatened Plant Species of Southern Ontario
Bibliographic record
Abstract
This thesis greatly benefitted from the help of many people in ineffable ways.Firstly, I need to thank my supervisor, Joseph Bennett, who from our very first conversation three years ago to the present has been nothing but encouraging, patient, understanding, and inspiring.I am so grateful that he has afforded me the opportunity to work on a research project concerning a conservation issue that is so important and close to us both.Throughout this process he has always provided helpful, thoughtful feedback on matters such as field work logistics, data analysis, writing, and all steps inbetween while showing the utmost concern for my mental and physical well-being.His kindness and generosity of time is unmatched.Except perhaps by Jenny McCune.Her incredible vision and motivation to begin this larger research project while a postdoc at the University of Guelph is what made my thesis research possible.From training me on the ins and outs of Maxent, to helping me with identification of pesky (yet loveable) asters and grasses, to providing me with carefully constructed comments on my thesis drafts (emphasis on the plural), she has been there every step of the way, always with a smile on her face.Jenny both encouraged and inspired me to consider different potential explanations and possibilities throughout my research and for all of this I cannot thank her enough.I would also like to sincerely thank my other two committee members, Andrew Simons and David Currie
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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 | 0.001 | 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 | 0.000 |
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".