Crops Journal Soil Biology of the Canadian Prairies
Bibliographic record
Abstract
Although some soil microorganisms cause plant diseases, most soil inhabitants are beneficial to crop production and the environment through processes like the fixing and cycling of nitrogen, biological pest control, formation and maintenance of soil structure (tilth), and degradation of agrochemicals and pollutants. This paper discusses the distribution of these organisms in prairie soils and how they are affected by soil and crop management practices. The discussion includes soil fauna and microorganisms, and singles out three groups of microorganisms that are particularly important in crop production: arbuscular mycorrhizal (AM) fungi, dark septate root inhabiting fungi, and rhizobia. The development of healthy diverse faunal and microbial communities in soil can be fostered by using soil management practices or systems like conservation tillage, crop rotation, proper nutrient management and application of organic manures when available. Major Groups of Soil Organisms Soil fauna Soil fauna are a diverse community of soil-dwelling animals. Fauna common in prairie soils include microscopic hair-like worms called nematodes, miniature earthworm-like animals called pot worms or enchytraeids, snails, slugs, springtails, insect larva, beetles, ants, spiders and earthworms. Fauna are functionally classified according to body width: microfauna are < 100 micrometers (μm) wide, mesofauna 100 μm to 2 mm wide, and macrofauna are> 2 mm
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".