Fatty acid profiles of more than 470 marine species from the Southern Hemisphere
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".