Spatial, temporal and technical variability in the diversity of prokaryotes and fungi in agricultural soils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Several studies have shown that Illumina MiSeq high-throughput sequencing can be used to measure the diversity of prokaryotes and fungal communities that provide ecosystem functions in agricultural soils. Pedoclimatic properties of soils, together with cropping systems and agricultural management practices, are major drivers of soil microbiome diversity. Their effects must be quantified and compared to technical variability to improve the relevance of observed effects and the indicators that may result from them. This study was conducted: 1) To assess the effects of three sources of technical variability on the soil prokaryotes and fungal diversity; 2) To identify a source of technical variability that can be used as a threshold to better assess crop management effects; 3) To evaluate the effects of spatial and temporal variability compare to a technical threshold in three crop management contexts, potato, corn/soybean and grassland. Technical variability was evaluated in a basis of sampling, soil DNA extraction and amplicon sequencing source of variability. Spatial variability was evaluated using composite bulk soil cores at four sampling points covering 2500 m² per field. Geolocated soils were also collected on three sampling dates during the growing season to evaluate temporal variability. A technical variability threshold was determined for the soil DNA extraction variability with a delta of Shannon index of 0.142 and 0.390 and a weighted UniFrac distance of 0.081 and 0.364 for prokaryotes and fungi, respectively. We observed that technical variability was consistently similar or lower than the spatial and temporal variabilities in each of the microbial communities. Observed variability was greater for the diversity of fungi and the crop system has a strong effect on temporal and spatial variability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 it