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Record W4408989344 · doi:10.1101/2025.03.26.645521

The nutritional value of invertebrate aquatic foods

2025· preprint· en· W4408989344 on OpenAlexaff
Jessica Zamborain‐Mason, Nisha Marwaha, Seo‐Hyun Yoo, Christina C. Hicks, James P. W. Robinson, Luisa R. Abucay, Laura G. Elsler, Jacob G. Eurich, Whitney R. Friedman, Jessica A. Gephart, M. Aaron MacNeil, Julia G. Mason, Maria Lourdes D. Palomares, Vina A Parducho, Katherine Seto, Kristin M. Kleisner, Daniel Viana, Christopher D. Golden

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsFisheries and Oceans CanadaDalhousie University
Fundersnot available
KeywordsInvertebrateValue (mathematics)FisheryEnvironmental scienceBusinessBiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Aquatic invertebrates are a diverse, nutrient-dense, and socio-ecologically important food whose contribution to human nutrition is frequently overlooked. We quantify their contribution to global nutrient supplies and estimate the nutrient content of >50,000 macroinvertebrate species. Current aquatic invertebrate production supplies the equivalent annual requirement for >6 billion people in terms of vitamin B12 and selenium; >1 billion people for copper, omega 3 fatty acids, iodine and zinc; and >100 million people for nutrients such as vitamins B2 and B3, iron, manganese, and magnesium. Nutrient composition differs among taxonomic groups, consumption patterns, and environmental and life-history factors. Our study highlights the benefits of integrating aquatic invertebrates into dietary portfolios across global societies, mainstreaming their nutritional importance in development projects, sustainability assessments and food policy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.212
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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