Methodological biases in estimates of macroalgal macromolecular composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it