Episode 089: How one gene determines the fate of a food web (with Matt Barbour)
Bibliographic record
Abstract
Can genes in single species act as keystones in ecosystems? What is AOP2, and how does it affect community composition and persistence? In this episode, we talk to Matt Barbour, a professor at the University of Sherbrooke, about “keystones” in biology. You’re probably familiar with the keystone species concept, but Matt’s research focuses on whether genes can play a similarly fundamental role in an ecosystem. In an incredible set of experiments, Matt and his colleagues used simple experimental food webs to find that the stability of these miniature complex systems was strongly associated to the genotype at one specific locus in the plant, Arabidopsis thaliana, called AOP2. The particular genetic variant led to complete breakdown of community stability, imbuing that gene with a keystone-like function. We talk to Matt about his recent publication in the journal Science and discuss how results from his simple lab setup relates to keystone effects in natural communities.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".