Bibliographic record
Abstract
Predators are faced with an uncertain world.The presence of anti-predator defences, means that any potential prey item may actually be unpalatable, outright toxic, difficult to catch, or cause harm.In order to deal with this uncertainty regarding profitability, predators need strategies to make good decisions on what to attack based on the information about potential prey available to them.I develop two models of optimal decision making for predators.The first deals with generalizing from experience to novel prey types: I develop a Bayesian model framework that treats generalization as a process of learning about the distribution of prey in the environment, and apply it to a problem in generalization.The second deals with startle displays: I develop an extension of signal detection theory to cases where continued examination is possible, and apply it to predators faced with startle displays.assistance over the course of my MSc.Every time I come out of a meeting with him, I feel better about the work I've done, and more optimistic about and better prepared for the next steps of my project.I've learned a great deal over the past two years, and I've had a great time doing it.I'm very grateful to have had the opportunity to work with Tom.I am also grateful to the other members of my advisory committee, Root Gorelick and Frithjof Lutscher, for their invaluable advice and assistance.Working in the Sherratt lab has been a great experience, and a lot of fun, which is thanks largely to all of the great people I've met here over the past two years.Thank you to all the lab members, and in particular to Lauren Efford, Eric Guerra-Grenier, and Sophie Potter.I'd like to especially thank Sophie, who has always been welcoming and supportive to everyone in the lab, and a great friend, and who also helped to select the name of the parameter Ξ. I'd also like to thank Joey Beauveais-Feisthauer, Blair Drummond, and LukeVolk for many weeks of entertaining conversation and fun problems.Finally,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".