Selection and stability of wave trains behind predator invasions in a model with non-local prey competition
Bibliographic record
Abstract
Journal Article Selection and stability of wave trains behind predator invasions in a model with non-local prey competition Get access Sandra M. Merchant, Sandra M. Merchant Department of Mathematics, Institute of Applied Mathematics, The University of British Columbia, 121–1984 Mathematics Road, Vancouver, BC, Canada V6T 1Z2 Search for other works by this author on: Oxford Academic Google Scholar Wayne Nagata Wayne Nagata * Department of Mathematics, Institute of Applied Mathematics, The University of British Columbia, 121–1984 Mathematics Road, Vancouver, BC, Canada V6T 1Z2 *Corresponding author: nagata@math.ubc.ca merchant@math.ubc.ca Search for other works by this author on: Oxford Academic Google Scholar IMA Journal of Applied Mathematics, Volume 80, Issue 4, August 2015, Pages 1155–1177, https://doi.org/10.1093/imamat/hxu048 Published: 16 October 2014 Article history Received: 18 October 2013 Revision received: 02 June 2014 Accepted: 12 September 2014 Published: 16 October 2014
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".