Betaine Yields from Marine Algal Species Utilized in the Preparation of Seaweed Extracts Used in Agriculture
Bibliographic record
Abstract
Ascophyllum nodosum, and to a lesser extent, Laminaria digitata, L. hyperborea and Fucus serratus, are marine algal species utilized in the commercial production of seaweed extracts used in agriculture. Betaines have been shown to be important constituents of these extracts, but there appears to have been no study made on whether there are variations in the betaine contents of these species based on either the place or date of collection. Samples of each of the four species were collected from widely separated areas at different times of the year. Also, in the case of A. nodosum, approximately monthly collections were made from one location. The betaines detected in the various collections of the same species showed little variation, although in the case ofA. nodosum, glycinebetaine was found as a minor constituent in some samples, but was not detected in others. Trigonelline was found in all the tested samples of the two Laminaria species; this is, to our knowledge, the first record of this betaine in marine algae. With the exception of trigonelline in the Laminaria species, the betaine yields from the various samples of L. digitata, L. hyperborea and F. serratus showed little variation, regardless of either the place or date of collection. The trigonelline contents of the Laminaria species collected at one location (Finavarra, Ireland), in particular of L. hyperborea, was substantially greater than those from the other places of collection. In the case of A. nodosum, the betaine yields from samples collected at one site (Dale, Pembrokeshire, UK) were significantly higher than those from the other places of collection, which were very similar to each other. There was no clear indication of seasonal variation in betaine yields from A. nodosum.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".