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Record W2946069691 · doi:10.4236/jsbs.2019.92004

Fish Processing and Its Energy Dynamics in Zambia

2019· article· en· W2946069691 on OpenAlexaff
Ebenezer Miezah Kwofie, Emmanuella Ellis, Sven Genschick, Michael Ngadi, Shakuntala H. Thilsted

Bibliographic record

VenueJournal of Sustainable Bioenergy Systems · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMcGill University
FundersInternational Fund for Agricultural Development
KeywordsFish processingEnvironmental scienceKilnEnergy consumptionFish <Actinopterygii>CharcoalWaste managementToxicologyBiologyEngineeringEcologyFisheryChemistry

Abstract

fetched live from OpenAlex

This study presents field data and results on local fish processing, its energy supply, and consumption in the Northern Province of Zambia. The study also evaluates the impact of processing conditions on fish quality and cost for the different processing systems available in the selected communities. The result shows that fuelwood is the primary source of energy for fish processing used either in a modified three-stone fire (MTSF) system or a recently developed kiln. The charcoal smoking alternative had the least fuel consumption, yet was not considered as the preferred option due to the high cost of the fuel, a smaller quantity of fish processed per batch and longer processing time. The result also revealed that irrespective of the system used, the type of fish being processed and the pre-smoking drying time had a significant impact on the total energy consumption. Overall, the smoking kiln was found to increase the quantity of fish processed by five folds, reduce fuel use by 48% and reduce the overall smoking time per kilogram fish processed by 39%. A relatively higher fish quality was obtained with the use of the smoking kiln in comparison with the MTSF and the charcoal smoking system. The use of the smoking kiln as a fish processing system is recommended because it improves the quality of the fish, can scale up fish processing due to its capacity and reduces energy use with its associated costs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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 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
Published2019
Admission routes1
Has abstractyes

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