SOFIA: Importance of agricultural practices on functional diversity of soil microbiota : first tracks of response with a diachronic study of biogeochemical cycles
Bibliographic record
Abstract
In the context of biodiversity conservation, climate changes and biogeochemical cycles understanding, the project SOFIA aims at understanding impacts of agricultural practices on functional diversity of soil microbiota, particularly via a change in nutrients availability to soil organisms. The field experiment consists in series of experimental treatments varying on anthropogenic pressure according to: crop rotation, fertilisation, residue management or soil tillage. We present the diachronic response of microorganisms with modification of soil management, allowing predictive information of agroecosystem functioning. Soils enzymes are interesting indicators to approach the release of nutrients for microbial and plants growth. This diachronic study (four years), following enzymatic activities linked to C, N, P and S cycles, allows hierarchizing firstly, the importance of the anthropic factors introduced and secondly the sensibility of each cycle to these factors. Before application of different agricultural practices, enzymatic activities were homogeneous on the site, whatever the cycle considered. After four years, the amounts of active enzymes were lower with conventional tillage than under reduced tillage and zero tillage. Significant increases appear after two years in the 0-5cm surface layer with reduced tillage, for C and N cycles. P cycle evolution started after two years but significant effects on P and S cycles were observed only after four years. Increased enzyme activity under reduced tillage systems may be related to increased available carbon and/or functional diversity of soils.Results will allow us to assess the ecosystem services potentially provided by the different cropping systems, and the rapidity of the turnover of the microbiota.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".