Unrecognized soil algal and cyanobacterial communities as a model, for herbicide risk assessment in agricultural soils
Bibliographic record
Abstract
Soil algae and cyanobacteria form microbiotic photosynthetic crusts, not only on desert\nsoils, but also in temperate cropped soils. Despite a scarce literature about their ecology in\nagricultural soils, they could constitute an original model to improve the consideration of\nmicrobial component in the environmental risk assessment of herbicides.\nThis work focused on the suitability of biochemical and molecular methods to characterize\nstructural and functional responses of algae and cyanobacteria, to herbicides, in agricultural\nsoils under different cropping systems, throughout laboratory and field approaches.\nCultural approaches are still helpful to isolate edaphic species for further ecotoxicological\ntests at the individual level. Photosynthetic pigments can provide biomass (chlorophyll a)\nor structural (pigment diversity) indicators. Based on chlorophyll a biomass, a modified\npollution-induced community tolerance (PICT) approach was developed. Several genetic\nmarkers were successfully applied, to estimate their community composition and diversity.\nToxic effects on algal and cyanobacterial biomasses and diversities have been evidenced at\ndoses sometimes below recommended field rates, in microcosm experiment. Overall, edaphic\nalgal and cyanobacterial communities showed a higher sensitivity to herbicides, compared\nto commonly studied soil bacterial and fungal community, in field monitoring. The PICT\napproach highlighted a higher tolerance to phenyl urea herbicides of the photosynthetic mi-\ncrobial communities in conventional versus organic soils. Characterization of the diversity\nof the algal and cyanobacterial communities, unravel links between some taxa and shift in\ntolerance levels. The structural stability of soil surface aggregates, considered as a functional\noutput of algae and cyanobacteria crusts, were disturbed by herbicide treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".