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Record W2483766535 · doi:10.5539/enrr.v6n3p58

Comparison of Fish Pond Waste Water with Manures under Garden Egg in Nigeria

2016· article· en· W2483766535 on OpenAlexvenueno aff
Dominic J. Udoh, Otobong B. Iren, Jeremiah E. Jonathan

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized block designCropManureChicken manureAnimal scienceEnvironmental scienceAgronomyFertilizerBiology

Abstract

fetched live from OpenAlex

<p class="1Body">Two field experiments were conducted from 2012 to 2013 cropping seasons to evaluate the efficacy and also determine the optimum rates of application for fish pond waste water in comparison with three other manures including one inorganic fertilizer and two organic manures under garden egg (<em>Solanum spp.</em>) crop.Garden egg was fertilized with pond waste water (PW), poultry manure (PM), pig manure (PG) and NPK15:15:15 at equivalent nitrogen (N) rates (0, 150, 300, 450 kg/ha). The treatments were arranged in a split plot under randomized complete block design (RCBD).The results obtained indicated a slight drop in soil pH but soil OM, total N, available P, exchangeable Ca and K increased generally with increasing rates of manures. The PW and PG treatments supported significantly (P<0.05) the highest yields of the crop compared to PM and NPK with the 150 and 300 kg/ha treatments recording the highest increase. These results have shown that under the high rainfall and acidic soil conditions of Akwa Ibom State in Nigeria, PW is useful for the improvement of soil conditions and achievement of higher crop yields when applied at rates that supply N in the range of 150 – 300 kg/ha.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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.

Study designObservational
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

Citations4
Published2016
Admission routes1
Has abstractyes

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