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Record W1878111795 · doi:10.5539/sar.v4n4p66

Maize Fertigation with Treated Olive Mill Wastewater: Effects on Crop Production and Soil Properties

2015· article· en· W1878111795 on OpenAlexvenueno aff
Maria I. Kokkora, Chryssoula Papaioannou, P. Vyrlas, Konstantinos Petrotos, Paschalis E. Gkoutsidis, Christos Makridis

Bibliographic record

VenueSustainable Agriculture Research · 2015
Typearticle
Languageen
FieldChemistry
TopicEdible Oils Quality and Analysis
Canadian institutionsnot available
FundersEuropean Social FundEuropean Commission
KeywordsFertigationFertilizerAgronomyWastewaterIrrigationEnvironmental scienceCropEnvironmental engineeringBiology

Abstract

fetched live from OpenAlex

<p>The present study investigates the potential of olive mill wastewater, treated by microfiltration and XAD4 macroporous resin, to be used as liquid fertilizer in maize production through a 2-year field experiment. The treated olive mill wastewater (T-OMWW) was applied at two rates of 25 t and 50 t per ha per year, supplemented with mineral fertilization. There was also a treatment involving the application of only T-OMWW at the rate of 50 t per ha per year, and an only mineral fertilizer treatment. Mineral fertilizers and T-OMWW were applied progressively through a drip irrigation system.</p> Maize grain and soil analysis showed that T-OMWW was capable to meet crop requirements in N, P and K, and increase soil N, P and K availability. There was a tendency for increasing soil Na and electrical conductivity (EC) using the higher rate of T-OMWW. Therefore, for sustainable agriculture, it may be safer to apply the T-OMWW at the lower rate of 25 t per ha<sup> </sup>per year, or use the higher rate of 50 t per ha<sup> </sup>every other year.

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)0.001
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 score0.080
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.279
Teacher spread0.243 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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