Review of: "Effect of Yogurt on Fluoride Induced Toxicity in Rabbits"
Bibliographic record
Abstract
the reader does not know the identity of the groups labelled with letters.The Abstract needs to be rewritten with less detail of results and more on methods.We do not know the units of excretion (mmol/L or mmol/day?)nor do we know the amount of F to induce fluorosis nor the amount of yogurt provided.Introduction: the authors target the role of microorganisms in gut absorption, which is fine, But they fail to mention that yogurt contains a lot of calcium and that others have shown that calcium (i.e., Ca++) reduces F-absorption by forming a compound (CaF2) that is difficult to absorb.Objectives: what is hypothesis?Are there secondary objectives?Methods:Groups -Groups C and D are not matched to a F dose.Animals are given 15g yogurt ( C) or 50 g yogurt (D).The only F+yogurt group is G.Why did you test different levels of yogurt?It should be explained in Objectives section.Yogurt is food with protein and electrolytes that can affect renal handling of nitrogen and Na, Cl and K. Creatinine-it is not clear why group F had lower Cr excretion.The amount of yogurt given in F is less than that of group D so the microbe explanation does not explain what could have happened.Conclusions: After examining all the results and the figures it appears group F showed a treatment effect.However the authors report group F was given 30 grams of yogurt .Only group G was F+yogurt.Did the authors mislabel the groups?
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".