Induced pathogenicity toward open-ocean diatoms by a newly isolated filterable bacterium <i>Ekhidna algicida</i> sp. nov.
Bibliographic record
Abstract
Abstract Phytoplankton are the base of marine food webs. They form intricate interactions with heterotrophic bacteria ranging from mutualistic to pathogenic that together impact oceanic carbon and nutrient cycling. Our understanding of these interactions in marine environments remains primarily limited to laboratory-based studies of model organisms. Here, we report the discovery and characterization of Ekhidna algicida sp. nov. strain To15, isolated from the oligotrophic Pacific Ocean (16°N, 140°W) based on its algicidal effect on the pelagic diatom Thalassiosira oceanica . Subsequent co-culture experiments demonstrate that E. algicida is lethal within days to a diverse array of diatoms, with the effect mediated by bacterial exudates that remain algicidal on their own against axenic T. oceanica cultures. Twenty additional algicidal Ekhidna strains were subsequently isolated from the Pacific Ocean. Our findings reveal E. algicida as a potentially widespread pathogen of diatoms, that can alter microbial community composition dynamics in pelagic ecosystems. Teaser A newly discovered Pacific Ocean bacterium can kill diatoms, revealing a hidden pathogenic role in open-ocean ecosystems.
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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) | 0.000 | 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".