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Record W2899077888 · doi:10.5376/ijh.2018.08.0018

Variation in the Determination of Macronutrient Absorption by Micronutrients in Certain Stionic Combinations of Grape

2018· article· en· W2899077888 on OpenAlexvenueno aff
S. D. Shikhamany, J.N. Kalbhor, T.S. Shelke, T. S. Mungare

Bibliographic record

VenueInternational Journal of Horticulture · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsnot available
Fundersnot available
KeywordsMicronutrientFood scienceChemistryVariation (astronomy)Absorption (acoustics)BiologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Investigations were carried out through a survey of vineyards of Thompson Seedless and 2A clone on their own root and Dog Ridge rootstock to identify those micronutrients which have positive relationship with major nutrient absorption and the stionic combinations in which such relationship exists. Relationship of major nutrient absorption with micronutrient status was highly complex, which could be attributed to variation in their absorption due to their relative abundance, variety and rootstock, and interactions among them. Results of the investigations indicated that none of the micronutrients could influence the absorption of N in any stionic combination. Fe, Mn, Zn and Cu together determined the absorption of Na highest in 2A clone on Dog Ridge rootstock, followed by the determination of P and S in Thompson Seedless on Dog Ridge. Mn contributed most towards the determination of P, K, Ca, Mg, S and Na across the stionic combinations. P absorption increased with higher levels of Mn and Cu respectively above 66.0 and 111.9 ppm, but decreased above 121.1 ppm of Zn in TS/DR. It increased with increasing levels of Mn above 129.8 ppm in 2A/OR and Zn levels above 121.2 ppm in 2A/DR. Higher levels of petiole Cu above 105.4 ppm and of Mn below 435.9 ppm increased K absorption in TS/DR. Levels of Mn above 210.2 ppm in 2A/OR, of Cu above 155.6 ppm and Mn at any level in 2A/DR were also associated with higher absorption of K. Calcium absorption increased with Fe levels above 116.7 ppm and Mn levels above 106.3 ppm in TS/OR. The increase in Ca absorption was limited to 466.7 and 500 ppm respectively of Fe and Mn in 2A/DR. Fe levels above 100 ppm resulted in increased Ca absorption in 2A/OR. Increased Mg absorption was associated with petiole Zn levels above 65.2 ppm in TS/OR, with any level of Mn and Zn levels up to 96.7 ppm in 2A/OR. It also increased with Zn levels above 112.9 ppm, but Mn levels up to 650 ppm in 2A/DR. Sulphur absorption increased with increasing contents of petiole Mn to any level and Cu levels above 149.3 ppm, but its increase was limited to 118.8 ppm of Zn in 2A/OR. Reduced absorption of Na was associated with increasing levels of Mn above 209.6 ppm in TS/OR, and with increasing levels of Fe, Mn and Zn respectively above 63.6, 257.1 and 117.1 ppm in 2A/OR. Increasing levels of Fe an Mn respectively above 516.6 and 380.8 ppm, but Zn levels below 66.7 ppm also reduced Na absorption in 2A/DR. Management of micronutrient levels above their threshold levels and below optimum levels corresponding to major nutrient absorption could help in increased absorption of P, K, Ca, Mg and S but reduced absorption of Na in different stionic combinations.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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