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Record W4303647448 · doi:10.1002/ecy.3888

Fatty acid profiles of more than 470 marine species from the Southern Hemisphere

2022· article· en· W4303647448 on OpenAlexaff
Peter D. Nichols, Heidi Pethybridge, Bowen Zhang, Patti Virtue, Lauren Meyer, Zahirah Dhurmeea, Lara Marcús, Jessica A. Ericson, Nicole Hellessey, Sharon L. Every, Kathryn E. Wheatley, Christopher C. Parrish, Pascale Eisenmann, Alastair M. M. Baylis, Corey J. A. Bradshaw, Stacy L. Bierwagen, Jock Young, Lydie I. E. Couturier, Christoph A. Rohner, Jasmin Groß, Courtney Waugh, Charles F. Phleger, Christine H. Jackson, George D. Jackson, Charlie Huveneers, Susan Bengtson Nash, Mina Brock, Peter Mansour

Bibliographic record

VenueEcology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTrophic levelBiologyFatty acidEcologyFood webNutrientOrganismEicosapentaenoic acidRange (aeronautics)Temperate climatePolyunsaturated fatty acidBiochemistry

Abstract

fetched live from OpenAlex

Abstract Lipid and fatty acid datasets are commonly used to assess the nutritional composition of organisms, trophic ecology, and ecosystem dynamics. Lipids and their fatty acid constituents are essential nutrients to all forms of life because they contribute to biological processes such as energy flow and metabolism. Assessment of total lipids in tissues of organisms provides information on energy allocation and life‐history strategies and can be an indicator of nutritional condition. The analysis of an organism's fatty acids is a widely used technique for assessing nutrient and energy transfer, and dietary interactions in food webs. Although there have been many published regional studies that assessed lipid and fatty acid compositions, many only report the mean values of the most abundant fatty acids. There are limited individual records available for wider use in intercomparison or macro‐scale studies. This dataset consists of 4856 records of individual and pooled samples of at least 470 different marine consumer species sampled from tropical, temperate, and polar regions around Australia and in the Southern, Indian, and Pacific Oceans from 1989 to 2018. This includes data for a diverse range of taxa (zooplankton, fish, cephalopods, chondrichthyans, and marine mammals), size ranges (0.02 cm to ~13 m), and that cover a broad range of trophic positions (2.0–4.6). When known, we provide a record of species name, date of sampling, sampling location, body size, relative (%) measurements of tissue‐specific total lipid content and abundant fatty acids, and absolute content (mg 100 g−1 tissue) of eicosapentaenoic acid (EPA, 20:5n3) and docosahexaenoic acid (DHA, 22:6n3) as important long‐chain (≥C20) polyunsaturated omega‐3 fatty acids. These records form a solid basis for comparative studies that will facilitate a broad understanding of the spatial and temporal distribution of marine lipids globally. The dataset also provides reference data for future dietary assessments of marine predators and model assessments of potential impacts of climate change on the availability of marine lipids and fatty acids. There are 480 data records within our data file for which the providers have requested that permission for reuse be granted, with the likely condition that they are included as a coauthor on the reporting of the dataset. Records with this condition are indicated by a “yes” under “Conditions_of_data_use” in Data S1: Marineconsumer_FAdata.csv (see Table 2 in Metadata S1 for more details). For all other data records marked as “No” under “Conditions_of_data_use,” there are no copyright restrictions for research and/or teaching purposes. We request that users acknowledge use of the data in publications, research proposals, websites, and other outlets via formal citation of this work and original data sources as applicable.

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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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