Experimental datasets on the extraction of functional ingredients from seaweeds for controlling bacterial infection
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
Seaweeds are gaining significant attention for their bioactive compounds, which hold great potential for use in food, cosmetics, and pharmaceuticals [1]. To avoid the use of toxic substances in the extraction process, there is a need for innovative and eco-friendly methods to exploit the highly potent raw seaweed biomass. Described herein are the datasets of how the particle size reduction of seaweeds positively enhanced the efficacy of green extraction in boosting the extraction yields of seaweed bioactive compounds. Different green extraction approaches were used to accumulate different seaweed particle sizes that were collected via grinding and sieving [2]. The total yields of carbohydrates, glucuronic acids, proteins, phenolics and flavonoids were quantified to evaluate the efficacy of the extraction strategies. The efficacy and safety usages of the extracts were assessed using different pathogenic bacterial strains and human cell lines, 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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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