Measuring the activity of Saccharomyces cerevisiae in relation to home-based additives by measured net weight loss
Bibliographic record
Abstract
This research study is to measure the activity of saccharomyces cerevisiae through selected additives which have been added in the hydration step of making bread dough. The saccharomyces cerevisiae is sensitive to sugars (Mazzoleni, S. et al.2015) and by using multiple possible additives that can be found at home, we can compare which ones give a healthier yeast and therefore a better rise to the dough. As the saccharomyces cerevisiae ferments, it consumes the sugars naturally in the dough and creates an acidic environment to maintain its growth and produces CO2 as a product of this reaction, which is the cause for the rising dough. This can be tracked by how active the yeast is to its mean weight loss by measuring the weight loss of the three separate batches and comparing the results through a Multiple Comparisons of Means: Tukey Contrasts test to see if the significance to what is added to what was added to help the fermentation process of the yeast. We can see that easily soluble sugars are the best choices for promoting the health of the saccharomyces cerevisiae in by the test with F(9,20)=14.49, p<0.0001.Keywords: Saccharomyces cerevisiae, Bread, Fermentation, Glucose, Baking
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".