The macromolecular composition of noncalcified marine macroalgae
Bibliographic record
Abstract
The macromolecular composition of macroalgae influences nutrient flow and food quality in aquatic ecosystems and the value of macroalgae species for human consumption, aquaculture, biofuels, and other applications. We used literature data (125 publications, 1,117 observations) and a hierarchal Bayesian statistical model to estimate the average macromolecular composition, protein, lipid, and carbohydrate of macroalgae as a whole and at the phylum level. Our focus was on marine, noncalcified macroalgae sampled from wild-grown populations in the field. We found that the median macromolecular composition is 9.98% protein, 2.7% lipid, 48.5% carbohydrate, and 31.8% ash as percent dry weight. We compared the median macromolecular content of macroalgae to microalgae and herbaceous plants and test for differences in macromolecular content across macroalgal phyla. Macroalgae were much more enriched in carbohydrate and minerals than the microalgae and lower in protein and lipid than many herbaceous plants. Rhodophyte macroalgae have significantly less lipid and more protein and the Ochrophyte macroalgae have significantly less protein than the average.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".