Comparison of three microquantity techniques for measuring total lipids in fish
Bibliographic record
Abstract
To measure lipids in juvenile and adult fishes, we refined three microquantity approaches (microgravimetric, microcolorimetric sulfophosphovanillan (SPV), and Iatroscan thin layer chromatography – flame ionization detection (TLC–FID)) that were originally developed to measure lipids in small aquatic invertebrates. We also evaluated their precision and comparability by quantifying the total lipid content of age-1+ walleye ( Sander vitreus ), yellow perch ( Perca flavescens ), and lake whitefish ( Coregonus clupeaformis ) collected in Lake Erie (US–Canada), Lake Michigan (USA), and Muskegon Lake (Michigan, USA). Our findings demonstrate that (i) microquantity approaches provide estimates of total lipids in juvenile and adult fishes similar to those of more traditional macroquantity approaches, (ii) the microcolorimetric SPV and microgravimetric approaches produce near identical estimates of total lipid content, and (iii) the Iatroscan TLC–FID approach underestimates total lipids relative to the other approaches for individuals with high lipid levels. Ultimately, our research makes available additional techniques for measuring total lipid content of fishes that are less expensive than traditional techniques, owing to a reduced need for large quantities of samples and solvents.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".