Identifying the core bacterial and fungal communities within four agricultural biobeds used for the treatment of pesticide rinsates
Bibliographic record
Abstract
Abstract Bacterial and fungal communities of four pesticide rinsate treatment biobeds constructed in Alberta and Saskatchewan, Canada were profiled via high throughput DNA sequencing to assess the effect of biobed depth and pesticide application on microbial community composition. Biobeds differed in geographical location and biobed design, and composition of pesticide rinsates (including herbicides, fungicides, and insecticides). All biobeds achieved similar treatment efficacy and supported greater bacterial diversity relative to fungal diversity, yet selected for similar abundant bacterial orders of Actinomycetales, Acidobacteria, Rhizobiales, and Sphingobacteriales and fungal taxonomic groups of Dothideomycetes, Eurotiales, Hypocreales, and Sordariales. Biobeds differed in the presence of unique and differentiated genera and operational taxonomic units. Biobed depth did not uniformly impact the diversity and/or the microbial community structure. Overall, pesticide application increased bacterial diversity, but had limited effect on the more variable fungal diversity, therefore suggesting broader implication for the effect of applied fungicides on biobed fungal communities. Highlights Biobeds support diverse bacterial and fungal communities Specific “core” bacterial and fungal taxa are abundant in biobeds of different design and treatment Microbial diversity is not directly linked with pesticide type or diversity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".