Contrasting ciliate species richness and abundance in soils derived from <i>Tectona grandis</i> and <i>Canella winterana</i>: testing the plant effect
Bibliographic record
Abstract
We still have a rudimentary understanding about the mechanism by which plant roots may stimulate soil microbial interactions. A biochemical model involving plant‐derived biochemical fractions, such as exudates, has been used to explain this “rhizosphere effect” on bacteria. However, the variable response of other soil microbial groups, such as protozoa, to the rhizosphere suggests that other factors could be involved in shaping their communities. Thus, two experiments were designed to (a) obtain a better understanding of the mechanism by which ciliate species richness and abundance differ among plant species and (b) to determine whether this mechanism is maintained via stimulatory and/or inhibiting factors associated with particular plant species. Bacterial and chemical slurries were reciprocally exchanged between two plant species known to differ in terms of ciliate species richness and abundance (i.e., Canella winterana and plantation Tectona grandis). The ANOVA showed that the bacteria plus nutrients, and the nutrients‐only treatment have no significant effect on the overall ciliate species richness and abundance when compared to the control treatment. However, the use of only colpodean species to increase the taxonomic resolution of treatment effects showed that bacterial slurries have a significant effect on colpodean ciliate species richness. These results suggest that for particular rhizosphere ciliates, biological properties, such as bacterial diversity or abundance, may have a strong influence on their diversity and possibly abundance. These results are consistent with a model of soil bacteria‐mediated mutualism between plants and protozoa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".