Biochar is colonized by select arbuscular mycorrhizal fungi in agricultural soils
Bibliographic record
Abstract
Arbuscular mycorrhizal fungi (AMF) colonize biochar in soils, yet the processes governing their colonization and growth in biochar are not well characterized. Biochar amendment improves soil health by increasing soil carbon, decreasing bulk density, and improving soil water retention, all of which can increase yield and alleviate environmental stress on crops. Biochar is often applied with nutrient addition, impacting mycorrhizal communities. To understand how mycorrhizas explore soils containing biochar, we buried packets of non-activated biochar in root exclusion mesh bags in contrasting agricultural soils. In this greenhouse experiment, with quinoa (Chenopodium quinoa) as the host plant, we tested impacts of mineral nutrient (as manure and fertilizer) and biochar addition on mycorrhizal colonization of biochar. Paraglomus appeared to dominate the biochar packets, and the community of AMF found in the biochar was a subset (12 of 18) of the virtual taxa detected in soil communities. We saw differences in AMF community composition between soils with different edaphic properties, and while nutrient addition shifted those communities, the shifts were inconsistent between soil types and did not significantly influence the observation that Paraglomus appeared to selectively colonize biochar. This observation may reflect differences in AMF traits, with Paraglomus previously identified only in soils (not in roots) pointing to predominately soil exploratory traits. Conversely, the absence of some AMF from the biochar implies either a reduced tendency to explore soils or an ability to avoid recalcitrant nutrient sources. Our results point to a selective colonization of biochar in agricultural soils.
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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 | 0.000 |
| 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) | 0.000 | 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 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".