A burrowing ecosystem engineer positively affects its microbial prey under stressful conditions
Bibliographic record
Abstract
Abstract Species that facilitate others under stressful conditions are often ecosystem engineers: organisms that modify or create physical habitat. However, the net effect of an engineering species on another depends on both the magnitude of the direct interactions (e.g., competition or predation) and the specific environmental context. We used a laboratory system to isolate the trophic and engineering impacts of a predator, the nematodeCaenorhabditis remanei, on its prey,Escherichia coliunder different levels of environmental stress. We predicted that under stressful surface conditions the nematodes would positively impact their prey by creating burrows which protected the bacteria. Colony plate counts ofE. coliindicated that there was a stress‐induced change in the net impact of nematodes on bacteria from neutral to positive. Predator engineering in the form of burrowing allowed larger bacteria populations to survive. We conclude that even in a simple two‐species system a predator can positively impact prey via ecosystem engineering.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".