Bibliographic record
Abstract
The knowledge on microbial diversity is one of the most revolutionary themes in science in the last years.The recent assess to the majority of microorganisms, based on cultureindependent methodologies, originated the term microbiome, one of the field most filled with novelties, and one of the most promising research line in the search for theoretical and technological innovations.Among all environments, soils harbor the bigger microbial diversity, being it composed by values of approximately 10 9 microbial cells per gram of soil, spread in something around 10 to 30 thousands distinct 'species'.This manuscript aim to demonstrate the advances promoted in the assessment of microbiomes in Brazilian soils, encompassing natural areas, such as mangroves and soils from the Northwest Caatinga; or soils used for agriculture, remarking the soils cultivated with sugarcane.Among the studies performed in natural areas, it is highlighted the advances achieved in mangrove soils, where the microbiome was described based on taxonomical units, and also in the functional features, found in this ecosystem under distinct levels of contamination.Concerning agricultural fields, soils cultivated with sugarcane have been addressed, supporting the detection of biogeographical patterns in the structuring of microbial communities, and being used for the description of microbial groups intimately interacting with plants.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".