Review of current hydroponic food production practices and the potential role of bioelectrochemical systems
Bibliographic record
Abstract
Abstract Hydroponic cultivation is an efficient, resource-saving technology that produces high yields of high-quality products per unit area without soil. While this technology can save water and fertilisers, water recirculation increases the accumulation of root exudates known to be toxic to the plant, causing growth inhibition. The usage of bioelectrochemical systems (BESs) is well-documented for wastewater treatment, desalination, contamination remediation, bioelectricity generation, etc. In this review we explore the issues associated with the usage of traditional approaches in detecting and removing the phytotoxic substances exudated from plant roots. Furthermore, we investigate the prospects of deploying BESs in hydroponic systems and highlight potential benefits and challenges. The application, feasibility and scalability of BES-hydroponic systems, as well as the possibility of integration with other technologies are all critically discussed. It is concluded that the use of BESs for hydroponic wastewater treatment and for real-time plant growth monitoring represents a novel and valuable strategy. This approach has the potential to overcome limitations of the existing treatment methods and contribute to the advancement of sustainable agriculture. Graphical abstract
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".