The Good, the Bad, and the Fungus: Insights into the Relationship Between Plants, Fungi, and Oomycetes in Hydroponics
Bibliographic record
Abstract
Hydroponic systems are examples of controlled environment agriculture (CEA) and present a promising alternative to traditional farming methods by increasing productivity, profitability, and sustainability. In hydroponic systems, crops are grown in the absence of soil and thus lack the native soil microbial community. This review focuses on fungi and oomycetes, both beneficial and pathogenic, that can colonize crops and persist in hydroponic systems. The symptomatology and mechanisms of pathogenesis for Botrytis, Colletotrichum, Fulvia, Fusarium, Phytophthora, Pythium, and Sclerotinia are explored for phytopathogenic fungi that target floral organs, leaves, roots, and vasculature of economically important hydroponic crops. Additionally, this review thoroughly explores the use of plant growth-promoting fungi (PGPF) to combat phytopathogens and increase hydroponic crop productivity; details of PGP strategies and mechanisms are discussed. The benefits of Aspergillus, Penicillium, Taloromyces, and Trichoderma to hydroponics systems are explored in detail. The culmination of these areas of research serves to improve the current understanding of the role of beneficial and pathogenic fungi, specifically in the hydroponic microbiome.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".