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Record W3046411744 · doi:10.18331/sfs2020.7.1.4

Estimation of live food consumption for Hippocampus barbouri and Hippocampus kuda

2020· article· en· W3046411744 on OpenAlexvenueno aff
Y.W. Len, Annie Christianus, Suchai Worachananant, Z. Muta Harah, Chou Min Chong

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsnot available
Fundersnot available
KeywordsHippocampusConsumption (sociology)BiologyNeuroscienceArt

Abstract

fetched live from OpenAlex

Seahorse is found worldwide in marine habitats such as the seagrass beds, coral reefs, mangroves and estuaries. Hippocampus barbouri and Hippocampus kuda are among the most traded seahorse species. In recent year, worldwide attention strongly support the establishment of seahorse aquaculture as to provide an alternative source of seahorse. However, the main bottleneck in succeeding seahorse aquaculture were problems faced in the culture of early stage juvenile, specifically the diet. The selection of suitable diet will contribute to the breeding success and larval rearing of seahorse. In this study the consumption of live food by new born, juvenile and adult seahorse were being investigated. H. barbouri and H. kuda commenced feeding at birth. The used of live food, namely the newly hatched Artemia nauplii was able support the growth and survival of new born and juvenile seahorses in specific tank systems. The results shows an increasing trend in which the increased of seahorse age and size (height), increases the average numbers of Artemia nauplii consumed. The minimum numbers of nauplii consumed by H. barbouri and H. kuda at 3 DAB with height 14.24±0.14 mm and 10.71±0.13 mm, were only 7 and 5 nauplii per feeding respectively. This study showed that Artemia nauplii can be used as live food for H. barbouri newborn stage to 28 DAB, while from newborn to 42 DAB for H. kuda. As for late juvenile stage of H. barbouri, at 90 DAB onwards, the used of adult Artemia instead of nauplii is highly recommended.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.207
GPT teacher head0.267
Teacher spread0.060 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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