Developing Native Fish to Control Spirogyra in Paddy Fields for Improving the Growth, Nutrient Uptake, and Physiological Characteristics of Oryza sativa L.
Bibliographic record
Abstract
Oryza sativa L. is the largest food crop in the world. The harmful filamentous green algae Spirogyra in paddy fields poses a serious threat to O. sativa yield. Therefore, biological control for Spirogyra is important for sustainable agricultural development. The native fish species Acrossocheilus yunnanensis can graze on Spirogyra and exhibits strong environmental adaptability, providing a novel approach to the biological control of Spirogyra. Therefore, we designed the O. sativa+Spirogyra+A. yunnanensis co-culture system to study the effects of A. yunnanensis on O. sativa growth and physiological characteristics. The results indicated that Spirogyra stress significantly inhibited O. sativa biomass accumulation, root length and plant height development, reduced photosynthetic efficiency, and increased the contents of oxidative stress markers including malondialdehyde (MDA) and hydrogen peroxide (H2O2). Interestingly, grazing of A. yunnanensis on Spirogyra increased the biomass of Oryza sativa by 58.60%, the root–shoot ratio by 78.01%, and the root length and plant height by 49.83% and 25.85%, respectively. Meanwhile, the soil nitrate nitrogen (NO3−-N), ammonium nitrogen (NH4+-N), and available phosphorus (AP) were enhanced, which improved O. sativa nutrient uptake and promoted photosynthetic pigment accumulation. This was manifested by an increase in chlorophyll content, net photosynthetic (Pn), transpiration rate, stomatal conductance (Gs), and intercellular CO2 concentration (Ci). Grazing of A. yunnanensis on Spirogyra alleviated the oxidative damage to O. sativa induced by Spirogyra, as evidenced by decreased malondialdehyde (MDA) and hydrogen peroxide (H2O2) level in both leaves and roots, along with increased protein content. This provides a new strategy for constructing a rice–fish symbiotic system by using indigenous fish species, achieving Spirogyra control and sustainable agricultural development.
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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 | 0.000 |
| 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) | 0.001 | 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".