Screening Common Bean Genotypes for Tolerance to Low Zinc Availability Using a Chelate-Buffered Hydroponics System
Bibliographic record
Abstract
A chelate-buffered hydroponics system was assessed for its ability to induce zinc (Zn) deficiency in common bean and for its usefulness as a tool to select genotypes tolerant of Zn deficiency stress. Twenty-two common bean genotypes were evaluated for tolerance to Zn deficient conditions using the buffered hydroponics system. Relative yield (comparing growth under low Zn to adequate Zn conditions) and Zn and phosphorus (P) accumulation in plants were measured. Significant Zn deficiency stress was induced in all of the genotypes with relative yields ranging from 11.5 to 52.6%. Six genotypes were identified as being tolerant to low Zn. These six genotypes (E 101, AND 684, LSA 102, SUG 55, PVA 773, and CENTRO) all had relative yields >29%, were able to accumulate >60 µg Zn per plant from solution and were better able to regulate P uptake to avoid excessive P accumulation than the other genotypes. In a second experiment, a subset of seven genotypes grown in the chelate-buffered hydroponics systems were compared directly to plants grown in field soil. The two systems correlated well for total dry weight (r = 0.57, P < 0.001), shoot Zn content (µg g−1) (r = 0.58, P < 0.001), and total Zn content (µg g−1) (r = 0.64, P < 0.001) when plants were grown under low Zn conditions.
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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) | 0.000 | 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".