Ecological and Industrial Implications of Dynamic Seaweed-Associated Microbiota Interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it