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Record W2936334670 · doi:10.5539/jas.v11n5p525

Boron Desorption Kinetic in Calcareous Soils

2019· article· en· W2936334670 on OpenAlexvenueno aff
Baydaa H. A. Al-Ameri

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Micronutrient Interactions and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsLoamSoil waterBoronCalcareousChemistryDesorptionBoric acidActivation energyEnvironmental chemistrySoil scienceEnvironmental scienceGeologyAdsorption

Abstract

fetched live from OpenAlex

Boron release (desorption) is one of the important factors use in estimating fertilizers use efficiency and management of boron in agricultural soils and correlation soil properties with it availability to plant. Chemical kinetics for boron desorption experiment was carried out of boric acid (source for boron fertilizer) under controlled conditions in three calcareous soils (clay, loamy and sandy loam) from central Iraq at three temperatures (278, 298 and 308 Kelvin), by using six mathematical and empirical equations viz. Zero order, First order, Second order, Parabolic diffusion, Power function and Elovich equation were used to study kinetic behavior of boron in calcareous soils. Elovich equation was the best for predication and describing boron released with highest correlation r = 0.942 and t value (9.004), and lowest SE.e (0.530) where boron release rate constant (K) was positively correlated with temperature (r = 0.978). Released rate constant (K) was increased from 0.329 to 0.561 hrs-1 as increasing incubation temperature from 278 to 308 K. Soils texture effect in boron release activation energy (Ea) in three soils under study. The overall average of boron release activation energy of three soils was 15.284 and the highest was 20.923 in clay soil and the lowest amount was 4.596 in sandy loam while loamy soil gave 20.332 KJ mol-1.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.153

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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
Published2019
Admission routes1
Has abstractyes

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