Remediation of deterioration in microbial structure in continuous <i>Pinellia ternata</i> cropping soil by crop rotation
Bibliographic record
Abstract
Pinellia ternata is a traditional Chinese herb that suffers from continuous cropping (CC), which significantly decreases both yield and quality. The influence of CC on the microbiome in P. ternata rhizosphere and the effects of remediation on microbiota by rotational cropping (CR) were assessed by Illumina high-throughput sequencing technology. CC tends to decrease the α-diversities as a function of cultivation time, whereas CR tends to increase them. Differentially abundant analysis showed that microbial structure was important in maintaining the health status of P. ternata rhizosphere. Results suggested that CC soils were mainly enriched for Pseudomonas, Rhizobium, and Streptophyta operational taxonomic units (OTUs), while the CR soils were mainly enriched for Rhizobium, Pseudomonas, Flavobacterium, Sphingomonas, Rhizobacter, and Arthrobacter OTUs. On the basis of the community dissimilarities, we grouped all sample replicates into three post hoc clusters in which soils were defined as healthy, health-suppressed, and health-depressed soils. The three soil types represented different soil physicochemical properties. The activities of the microbiome features, including ammonia oxidizer, sulfate reducer, nitrite reducer, dehalogenation, xylan degrader, sulfide oxidizer, nitrogen fixation, atrazine metabolism, chitin degradation, degraded aromatic hydrocarbons, and chlorophenol degradation, were also considerably different among the three soils.
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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 | 0.000 | 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 | 0.000 |
| 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 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".