Evaluation of bacterial and archaeal communites under different agricultural land management practices in southern Québec soils
Bibliographic record
Abstract
This thesis represented the first attempt to investigate the effect of different tillage and crop residue management regimes on the genetic and functional diversity of soil Bacteria and Archaea in a corn agroecosystems in southern Québec. Soils were collected from a long-term (>15 year old) agricultural experiment with three tillage treatments- no-till, reduced tillage, and conventional tillage (mouldboard plowing) and two levels of residue input- with residues (corn roots, stems, and leaves) versus without residues (corn roots and little above ground residues). PCR-DGGE analysis of soil DNA extracts indicated that there was no significant difference of Bacterial and Archaeal communities in the different soil treatments. The potential for atrazine degradation was determine using a soil microcosm mineralization assay. The results of this experiment indicated that all the treatments had almost the same effect on atrazine mineralization. Functional gene microarray analysis of soil microorganisms affected by different treatments showed no clear difference among the different treatments. Microscopic analysis (CARD-FISH and DTAF) indicated that biomass and numbers of Bacterial and Archaeal were not significantly changed as a consequence of different treatments on agricultural soils in southern Québec. In conclusion, this study indicated that the different tillage practices (no-tillage, reduced tillage and conventional tillage) and crop residue managements (with residue and without residue) did not change soil microbial genetic/functional diversity, atrazine degradation, and microbial biomass.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".