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Record W2167104831 · doi:10.4141/p06-029

Effect of ammonium/nitrate ratio on growth and development of avocado plants under hydroponic conditions

2007· article· en· W2167104831 on OpenAlexvenueno aff
Philippe Lobit, Luis López‐Pérez, Raúl Cárdenas‐Navarro, Vilma Castellanos‐Morales, R. Ruíz-Corro

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenDry matterAmmoniumShootNitrateChemistryAmmonium nitrateNutrientHorticultureCarbon fibersAgronomyBotanyBiologyMathematics

Abstract

fetched live from OpenAlex

An experiment was carried out to study the effects of the proportion of ammonium (NH 4 + ) and nitrate (NO 3 − ) in the nutrient solution on avocado plants in a hydroponic system. Five treatments were applied, consisting of the following proportions of ammonium to nitrate ions in the solution: T0 = 0:4, T1 = 1:3, T2 = 2:2, T3 = 3:1 and T4 = 4:0 at a constant nitrogen concentration of 4 mM. After 4 months, the plants were harvested and fresh and dry matter accumulation, leaf area, and leaf nitrogen and carbon content were measured. An architectural description of the shoot (length, number of nodes and branching) was made. Dry matter of shoots and leaves all decreased with increasing NH 4 + concentrations. In contrast, water content in aerial organs increased with NH 4 + concentration. Nitrogen and carbon content and the relationship between dry matter production and leaf nitrogen were not significantly affected by the treatments. The architectural analysis showed that the shoots developed over time in one or two distinct growth units. While nitrogen form affected both growth units, the second one was more severely affected, showing decreased length, number of nodes, and branching when NH 4 + concentration increased. Key words: Avocado, nitrogen, nitrate, ammonium, development

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 score0.557
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.234
Teacher spread0.215 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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