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Record W3130410454 · doi:10.1111/1750-3841.15647

Gonadal development and biochemical composition of Chinese mitten crabs ( <i>Eriocheir sinensis</i> ) from four sources

2021· article· en· W3130410454 on OpenAlexaff
Shihui Wang, Yingze Wang, Xugan Wu, Xubin Zhang, Jinshan Zhao, Jingming Yang, Yongxu Cheng

Bibliographic record

VenueJournal of Food Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsMinistry of Agriculture
FundersEarmarked Fund for China Agriculture Research System
KeywordsEriocheirGonadosomatic IndexBiologyDocosahexaenoic acidChinese mitten crabEicosapentaenoic acidArachidonic acidLinoleic acidDecapodaAnimal scienceShrimpCrustaceanFatty acidFood scienceBotanyFisheryPolyunsaturated fatty acidBiochemistryFecundityEnzymePopulation

Abstract

fetched live from OpenAlex

The gonadal development; edible tissue ratio; and proximate, fatty acid, and free amino acid composition were examined and compared among Eriocheir sinensis samples from four sources. The gonadosomatic index (GSI) of Heilongjiang (HLJ) crabs was significantly higher than that of the other crabs from September to October (P < 0.05). Of all the edible tissues of E. sinensis, HLJ crab had the highest linolenic acid (18:3n3, LNA) and arachidonic acid (20:4n6, ARA) contents (P < 0.05), while Qinghai (QH) crab had the highest eicosapentaenoic acid (20:5n3, EPA) content (P < 0.05). The highest docosahexaenoic acid (22:6n3, DHA) and DHA/EPA contents were observed in Shandong (SD) crabs (P < 0.05), and the highest linoleic acid (18:2n6, LA) content was found in Shanghai (SH) crabs (P < 0.05). In conclusion, E. sinensis inhabiting relatively low-temperature regions (northern China) can initiate gonadal development earlier, and the quality of E. sinensis living in paddies and lakes is better than that of crabs living in ponds.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.132

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 head0.015
GPT teacher head0.210
Teacher spread0.195 · 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 designBench or experimental
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

Citations18
Published2021
Admission routes1
Has abstractyes

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