Assemblages of arbuscular mycorrhizal fungi in tropical humid and dry forests in the Northeast of Brazil
Bibliographic record
Abstract
Tropical forests concentrate most of the world’s biodiversity. In the Northeast of Brazil it is possible to record mosaics of tropical dry and moist forests growing nearby but with completely different biotic and abiotic characteristics. These forests are constantly threatened by intense environmental devastation that affect not only above-ground communities but also those hidden below-ground, such as arbuscular mycorrhizal fungi (AMF), which act in maintaining ecosystem balance. This work aimed to determine the composition of native AMF communities in areas of humid forest (HF) and dry forest (DF) in northeastern Brazil. A total of 70 taxa of AMF were identified in the study areas, with Acaulospora (17) and Glomus (16) being the most representative genera. The sampling effort allowed the evaluation of 70%–73% of the species estimated for the areas. AMF communities differed between HF and DF areas. Species of the genus Glomus occurred predominantly in humid forest areas, whereas representatives of the order Gigasporales were associated more specifically with the dry forest area. Four soil attributes showed approximately 50% correlation with the composition of the AMF community (silt, clay, K, and CEC). Humid and dry forest areas presented a high diversity of AMF, and the soil properties were an important factor for the community composition of these fungi.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".