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Record W2439224035 · doi:10.21061/ijra.v12i1.1352

Water Quality and Nutrient Aspects in Recirculating Aquaponic Production of the Freshwater Prawn, Macrobrachium rosenbergii and the Lettuce, Lactuca sativa

2011· article· en· W2439224035 on OpenAlexaff
Hamid Khoda Bakhsh, T. Chopin

Bibliographic record

VenueInternational Journal of Recirculating Aquaculture · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInnovations in Aquaponics and Hydroponics Systems
Canadian institutionsUniversity of New Brunswick
FundersUniversiti Malaysia TerengganuUniversiti Putra Malaysia
KeywordsMacrobrachium rosenbergiiLactucaPrawnFisheryAquaponicsBiologyWater qualityAquacultureNutrientEnvironmental scienceAgronomyFish <Actinopterygii>Ecology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of different nutrients and their ability to improve the production of Macrobrachium rosenbergii and Lactuca sativa in a prototype recirculating aquaponic (RA) system. Experimental units were set up with different amounts of supplemented organic and inorganic (complex minerals) nutrients to carry out the study. The results indicated that desirable growth of M. rosenbergii might be possible in RA systems when supplied sufficient levels of macro-micro nutrients. Analyses of nutrients in the prawn culture tanks demonstrated that ammonia and nitrate concentrations were critical in maintaining proper water quality during the culture period. Five-day biological oxygen demand (BOD5) increased significantly with the increased loading of organic supplement in the rearing tanks. A significant linear relationship of chlorophyll a and N:P ratio was observed among the treatments. The combination of complex minerals and organic chicken manure (CM15) displayed a higher N:P ratio, maximal total yield and did not show adverse effects of NH3 concentrations and other important water quality parameters.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.182

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.031
GPT teacher head0.257
Teacher spread0.226 · 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 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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Recirculating AquacultureSame topicInnovations in Aquaponics and Hydroponics SystemsFrench-language works237,207