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Record W1967191156 · doi:10.1081/pln-120027654

Screening Common Bean Genotypes for Tolerance to Low Zinc Availability Using a Chelate-Buffered Hydroponics System

2004· article· en· W1967191156 on OpenAlexaff
J. Diane Knight, Diego Gangotena, Deborah L. Allan, Carl J. Rosen

Bibliographic record

VenueJournal of Plant Nutrition · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHydroponicsZincShootPhosphorusHorticultureChelationGenotypeChemistryPlant physiologyPlant nutritionDry weightZinc deficiency (plant disorder)Phosphorus deficiencyBiologyAgronomyAnimal scienceBotanyNutrientBiochemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.237
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

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