Methodological biases in estimates of macroalgal macromolecular composition
Bibliographic record
Abstract
Abstract Interest surrounding the use of macroalgae macromolecules for food products, biofuels, or other industrial applications is growing. As researchers search for macroalgae with especially high protein, lipid, carbohydrate or fibre content, the demand for a suite of standardized and unbiased methods for quantifying macroalgae macromolecules increases. Using data from available scientific literature, we evaluated the biases of the major methods used to determine macroalgal macromolecular content, as well as the sample drying methods employed. We found that drying at room temperature prior to analysis resulted in the highest estimates of protein and carbohydrate, and that freeze‐drying provided the highest estimates of lipid. Using nitrogen content and the standard conversion factor to calculate protein in macroalgae (N × 6.25 method) overestimates protein content compared to protein assays such as the Bradford ( ) or Lowry ( ) assays. The Bligh and Dyer ( ) lipid extraction method was found to have a yield nearly two‐fold higher than other standard methods. For carbohydrates, the By Difference and Prosky et al. ( ) methods provide estimates up to five‐fold higher than other common methods used to determine carbohydrate and fibre. Based on these results we recommend using protein assays as opposed to nitrogen content assays to determine protein content, the Bligh and Dyer lipid extraction method for lipids, and the By Difference and Prosky method for carbohydrate and fibre, respectively.
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 | 0.054 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".