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Record W4386977079 · doi:10.5376/ija.2023.13.0008

The Effects of Manual

2023· article· en· W4386977079 on OpenAlexvenueno aff
Juan Carlos del Valle, Csar Molina, Mguel Jover

Bibliographic record

VenueInternational Journal of Aquaculture · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersUniversitat Politècnica de València
KeywordsShrimpHectareStockingAnimal scienceFisheryBiologyEnvironmental scienceEcologyAgriculture

Abstract

fetched live from OpenAlex

The objective of the current study was to analyze the performance of white shrimp fed using three feeding systems: manual, time feeders and sound feeders, in thirteen commercial semi-intensive farms in three areas of Guayas province (Ecuador) using 535 production lots during the period 2015~2017. The size of the ponds ranged between 3 and 23 hectares, with a depth of 1.2 m, in which the exchange of water was around 1 % a day. The initial weight of the shrimp was 0.04~0.29 g, and stocking density was around 10 shrimp per square meter. The three feeding systems used commercial diets with 35% protein content. The time feeder system gave a higher shrimp yield (1 631 kg/ha) than manual feeding (1 539 kg/ha) and the sound feeder system (1 483 kg/ha). The best results for survival were obtained with the manual system (61.7%) and the time feeder (62.9%) in comparison to sound feeder (57.0%). Current performance results with acoustic systems were lower than reported by other authors, probably because the number of feeders per hectare was low, reducing the accessibility of shrimp to feed, in fact daily feeding supplied was not improved as has occurred in other studies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.091

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 studies0.0000.000
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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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