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Depth-Wise Distribution of Boron in Soil Under Groundnut-Cabbage Cropping Sequence

2024· article· en· W4401288692 on OpenAlexaff
Bhavin Suthar, Dileep Kumar, Kamlesh C. Patel, Anita Shukla, Sanjib Kumar Behera, J. C. Shroff, R.A. Patel

Bibliographic record

VenueJournal of the Indian Society of Soil Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRice Cultivation and Yield Improvement
Canadian institutionsNutrition International
Fundersnot available
KeywordsVeterinary medicineCroppingDistribution (mathematics)BiologySequence (biology)AgronomyMathematicsNon-invasive ventilationForensic scienceAgricultureEcologyGeneticsMedicine

Abstract

fetched live from OpenAlex

The present study aimed to assess the depth-wise distribution of boron (B) fractions in soil under six years B application schedues in groundnut-cabbage cropping system, conducted during the year 2014-2015 to 2019-2020 at Anand Agricultural University, Anand, Gujarat, India. The surface as well as sub-surface soil samples (0-15, 15-30, 30-45, 45-60, 60-75 cm) were collected from each plot. The treatments involving combinations of three B application frequencies (application only in the first year, alternate year and every year) and four B application rate (0.5, 1.0, 1.5 and 2.0 kg B ha-1) with a total treatment combinations of 13 including one control. The B was applied to the soil as borax to groundnut crop only. The effects of B application schedules on B fractions, i.e., readily soluble B, specifically adsorbed B, oxide bound B, organically bound B and residual B were significant under different soil depths. The amount of B fractions was increased with the level of B application. The surface soil (0-15 cm) had the greatest B concentration across all fractions, and the amount of B applied. While the residual B fraction increased with soil depth, and the readily soluble B, particularly adsorbed B and oxide bound B fractions dropped.

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 score0.897
Threshold uncertainty score0.178

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 science0.0010.000
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 head0.036
GPT teacher head0.266
Teacher spread0.230 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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