Soil Biodiversity and Root Pathogens in Agroecosystems
Bibliographic record
Abstract
Soil ecosystem is a living and dynamic environment, habitat of thousands of microbial species, animal organisms and plant roots, integrated all of them in the food webs, and performing vital functions like organic matter decomposition and nutrient cycling; soil is also where plant roots productivity represent the main and first trophic level (producers), the beginning of the soil food web and of thousands of biological interactions. Agroecosystems are modified ecosystems by man in which plant, animal and microorganisms biodiversity has been altered, and sometimes decreased to a minimum number of species. Plant diseases, including root diseases caused by soil-borne plant pathogens are important threats to crop yield and they causes relevant economic losses. Soil-borne plant pathogens and the diseases they produce can cause huge losses and even social and environmental changes, for instance the Irish famine caused by Phytophthora infestans (1845–1853), or the harmful ecological alterations in the jarrah forests of Western Australia affected by Phytophthora cinnamomi in the last 100 years. How can a root pathogen species increase its populations densities at epidemic levels? In wild ecosystems usually we expect the soil biodiversity (microbiome, nematodes, mycorrhiza, protozoa, worms, etc.) through the trophic webs and different interactions between soil species, are going to regulate each other and the pathogens populations, avoiding disease outbreaks. In agroecosystems where plant diseases and epidemics are frequent and destructive, soil-borne plant pathogens has been managed applying different strategies: chemical, cultural, biological agents and others; however so far, there is not enough knowledge about how important is soil biodiversity, mainly microbiome diversity and soil food webs structure and function in the management of root pathogens, in root and plant health, in healthy food production, and maybe more relevant in the conservation of soil as a natural resource and derived from it, the ecosystem services important for life in our planet.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".