A generalist microbial predator shows innate attraction to more profitable prey, but does not learn from experience
Bibliographic record
Abstract
Abstract How do generalists adjust to variation in prey abundance and profitability and seek out their preferred prey? We investigated this question in the soil protist Dictyostelium discoideum , a generalist predator of many species of bacteria. Despite their generalist diet, amoebas proliferate more quickly on some bacteria than on others. We tested amoeba chemoattraction towards 23 bacterial species and found that they are generally more attracted to the more profitable prey bacteria. Naïve amoebas were also preferentially more attracted to an edible mutant rather than the inedible wild type of a soil Pseudomonas isolate. These results suggest that D. discoideum amoebas have an innate prey preference that is adaptive. We also tested how experience with different prey bacteria affects chemoattraction in amoebas. Given the huge number of bacterial species in soil, learning from experience should be advantageous. However, we found no evidence that experience with prey bacteria affects preference. Our results suggest that generalist amoebas are innately attracted to the more profitable prey bacteria and this innate attraction cannot be overridden by recent experience.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".