Bibliographic record
Abstract
The manufacturing company behind One Shot Keto claims that a recent study published by diabetes, obesity, and metabolism journal found that One shot keto supported burning fat for energy instead of carbohydrates greatly increasing weight loss and energy. The supplement is designed to drive your body into a fat-burning state known as ketosis and thus prevent gaining more weight. Click Here https://apnews.com/press-release/ts-newswire/north-america-canada-united-states-5c788db671855a0db4103fdd44cb6927\n\n \n\nhttps://sites.google.com/view/one-shot-keto-canada-price/One-Shot-Keto\n\n \n\nAt this time, your body burns fat for energy. When the body uses fat as fuel, ketone bodies are produced. These ketone bodies are utilized as an energy source. One Shot Keto acts as an important source of fuel. It can easily cross the blood-brain barrier, so it acts as a powerful source of energy for your brain too. It can also trigger the release of chemicals called neurotrophins, which performs neuron function and synapse formation too.Click Here https://apnews.com/press-release/ts-newswire/north-america-canada-united-states-5c788db671855a0db4103fdd44cb6927\n\n \n\nhttps://sites.google.com/view/one-shot-keto-canada-price/One-Shot-Keto
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.024 |
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".