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Record W2753775624 · doi:10.5539/mas.v11n9p165

Aquaponic Integration and Automation – A Critical Evaluation

2017· article· en· W2753775624 on OpenAlexvenueno aff
Noel Scattini, Stanislaw Maj

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAquaponicsHydroponicsAquacultureEnvironmental scienceAgricultureAgricultural engineeringFish <Actinopterygii>NutrientBiotechnologyBusinessAgronomyBiologyEcologyEngineeringFishery

Abstract

fetched live from OpenAlex

Aquaponics is technology developed from the aquaculture industry that integrates intensive farming of fish and utilizes plants (integrates hydroponics) in a continuous closed loop to clean the water for the fish. The plants clean the water of nitrate (waste form is initially ammonia) which has been converted into a form that is not toxic to fish by bacteria and is accessible to plants. Hydroponics technology is a technique used to grow plants and vegetables that does not incorporate soil, but nutrients that are dissolved in water and plants are either floated or treated with a nutrient film delivered to the roots by a variety of processes. These technologies are becoming more popular with communities and governments as the preservation of water becomes more of an issue in environments where water is becoming restricted in supply and has to be supplemented by mechanical means such as desalination, which comes at additional cost to consumers. The technologies have had a great amount of research interest to find parametres and ranges that the systems require to function successfully yet also remain productive with certain crop varieties. The successful diversification of crop varieties could increase the viability of commercial aquaponics, which could be achieved with the use of optimized advanced process control strategies.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.046
GPT teacher head0.316
Teacher spread0.270 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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