Infochemicals in terrestrial plants and seaweed holobionts: current and future trends
Bibliographic record
Abstract
Since the holobiont concept came into the limelight ten years ago, we have become aware that responses of holobionts to climate change stressors may be driven by shifts in the microbiota. However, the complex interactions underlying holobiont responses across aquatic and terrestrial ecosystems remain largely unresolved. One of the key factors driving these responses is the infochemical-mediated communication in the holobiont. In order to come up with a holistic picture, in this Viewpoint we compare mechanisms and infochemicals in the rhizosphere of plants and the eco-chemosphere of seaweeds in response to climate change stressors and other environmental stressors, including drought, warming and nutrient stress. Furthermore, we discuss the inclusion of chemical ecology concepts that are of crucial importance in driving holobiont survival, adaptation and/or holobiont breakdown. Infochemicals can thus be regarded as a 'missing link' in our understanding of holobiont response to climate change and should be investigated while investigating the responses of plant and seaweed holobionts to climate change. This will set the basis for improving our understanding of holobiont responses to climate change stressors across terrestrial and aquatic 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".