Ecological and Industrial Implications of Dynamic Seaweed-Associated Microbiota Interactions
Bibliographic record
Abstract
Seaweeds are broadly distributed and represent an important source of secondary metabolites (e.g., halogenated compounds, polyphenols) eliciting various pharmacological activities and playing a relevant ecological role in the anti-epibiosis. Importantly, host (as known as basibiont such as algae)-microbe (as known as epibiont such as bacteria) interaction (as known as halobiont) is a driving force for coevolution in the marine environment. Nevertheless, halobionts may be fundamental (harmless) or detrimental (harmful) to the functioning of the host. In addition to biotic factors, abiotic factors (e.g., pH, salinity, temperature, nutrients) regulate halobionts. Spatiotemporal and functional exploration of such dynamic interactions appear crucial. Indeed, environmental stress in a constantly changing ocean may disturb complex mutualistic relations, through mechanisms involving host chemical defense strategies (e.g., secretion of secondary metabolites and antifouling chemicals by quorum sensing). It is worth mentioning that many of bioactive compounds, such as terpenoids, previously attributed to macroalgae are in fact produced or metabolized by their associated microorganisms (e.g., bacteria, fungi, viruses, parasites). Eventually, recent metagenomics analyses suggest that microbes may have acquired seaweed associated genes because of increased seaweed in diets. This article retrospectively reviews pertinent studies on the spatiotemporal and functional seaweed-associated microbiota interactions which can lead to the production of bioactive compounds with high antifouling, theranostic, and biotechnological potential.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".