Root Endophytes of Coffee (<i>Coffea</i> <i>arabica</i>): Variation Across Climatic Gradients and Relationships with Functional Traits
Bibliographic record
Abstract
The root microbiome of Central American coffee trees was studied from four different sites experiencing different annual temperatures and precipitation levels, sampling from plots grown conventionally and under agroforestry management (with shade trees). Total community DNA was separately extracted from roots from four trees sampled from each site/management pair and analyzed using terminal restriction fragment polymorphism analysis and also next generation sequencing (Illumina) of fungal and bacterial ribosomal amplicons. Community profiles were analyzed for site and management effects and correlations to environmental parameters and tree leaf and root economic traits. Communities of both bacteria and fungi varied with site locations, but were not impacted by management system type. They also both varied strongly with environmental parameters. Fungal communities also showed significant variation that could be attributed to plant leaf and root traits. Pooled DNA samples from each site/management regime were used to generate amplicons for next generation sequencing to determine the dominant members of the coffee root microbiome at these locations. Core bacterial genera included Pantoea, Enterobacter, and Burkholderia, while fungal core communities were dominated by members of Cladosporium, Penicillium, Exidiopsis, Trechispora, and Mycena. The potential ecological function of these microbial associates is discussed.
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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 | 0.000 |
| 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".