Total and arbuscular mycorrhizal fungal communities in the first 3 years after the collapse of the Fundão Dam: are we on the ecosystem recovery pathway?
Bibliographic record
Abstract
After the collapse of Fundão Dam in Mariana‐MG, Brazil, the discharge of iron ore mining waste into the Doce River watershed negatively impacted the landscape. Monitoring the composition and species richness of soil microbiota may be useful bioindicators of ecosystem recovery. This study aimed to compare soil chemical properties, total fungal species, and arbuscular mycorrhizal fungi (AMF) in sites unaffected and affected by mining tailings in the first 3 years after the collapse of Fundão Dam. Soil and root samples were collected in dry and rainy seasons over 3 years in unaffected (adjacent forest and pasture) and affected areas (REC1, REC2, and PASTrec) by mine tailings. Changes in soil chemical properties over the sampling period were measured by routine chemical analyses. Total fungi in the soil was determined by high‐throughput sequencing. AMF community was evaluated using spore number, root colonization, and denaturing gradient gel electrophoresis. Affected sites had higher ranges of pH and lower soil organic matter than unaffected sites. Revegetation had a positive effect on soil fungal community. Increased similarity in AMF DGGE analysis was observed in the two sampling sites over time. A high similarity of total fungi and AMF was observed between REC and pasture areas, suggesting that revegetation strategy employed may be heading towards a pasture condition. Thus, post‐disturbance analysis of this study was important to evaluate of ecosystem recovery affected by the rupture of iron ore mining dam and demonstrated that the soil microbiota was a sensitive bioindicator in this long path of ecosystem recovery.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".