Occurrence and species richness of mycorrhizal fungi in soil under different land use
Bibliographic record
Abstract
This study aimed to assess the impact of different land uses on the spore density and richness of arbuscular mycorrhizal fungi (AMF) in a Red Latosol of a tropical savanna biome in Brazil (“Cerrado”). Ten soil samples and roots were obtained from a riparian forest, a pasture, an annual crop production system with no tillage, and a coffee plantation in dry and wet seasons. Spores were extracted, counted, and identified from field soils, and roots were stained to assess mycorrhizal colonization. A total of 42 AMF species were detected in all four land uses. The genus Acaulospora had the largest number of species (18), followed by Glomus (6) and Gigaspora (5). Gigaspora decipiens and Gigaspora margarita were present in all areas, regardless of the sample period. Gigaspora decipiens was the species most frequently recovered and contributed to the majority of spores in coffee plantations and riparian forest. Cetraspora pellucida was dominant in the area of no tillage and Acaulospora tuberculata in the pasture. Low species richness was detected in soils under the coffee plantation, with AMF communities dominated almost exclusively by members of Gigasporaceae. We conclude that the area under the coffee plantation caused a reduction in the richness of AMF species and promoted a dominance of a single family AMF compared with the pasture area.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".