MétaCan
Menu
Back to cohort
Record W3202990461

AVERAGE DAILY FLUID INTAKE OF STUDENTS ENROLLED IN A PRIVATE SECTOR UNIVERSITY IN ISLAMABAD; A CROSS SECTIONAL STUDY

2019· article· en· W3202990461 on OpenAlexvenueno aff
Abdul Momin, Hina Zulfiqar, Usama Ejaz, Ayesha Gull, Dilara Maqbool, A Fatim, Zaiya Waseem, Ume Kalsoom, Anam Khurshid

Bibliographic record

VenueAdvanced Food and Nutritional Sciences · 2019
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFluid intakeCross-sectional studyMedicineFood intakeEnvironmental healthInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: Appropriate fluid intake is necessary to carry out all the essential biochemical reactions taking place inside a human body.Methodology: The current study was carried out at the Department of Diet and Nutritional Sciences, The University of Lahore, Islamabad Campus in which a cross sectional study design was used. A total of one hundred students were chosen using the technique of convenience sampling. Students who gave consent were made a part of the study while those suffering from any chornic diseases were excluded. For the purpose of determination of avergae daily fluid intake of students, twenty-four hour dietray recall was taken. Food composition tables were used to calculate the exact amount of fluids contributed by each food item or beverage.Results: The results of the study revealed that only 12% students were having an avergae daily intake of fluid either equivalent to or more than the recommended intake. On the other hand, reamining 88% students were having an average daily fluid intake of less than the recommended intake.Conclusions: The current study concluded that the average daily fluid intake of 88% students enrolled at the Department of Diet and Nutritional Sciences, The University of Lahore, Islamabad Campus which was not appropriate in comparison to the recommended intake.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.332
Teacher spread0.312 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Food and Nutritional SciencesSame topicHealth and Well-being StudiesFrench-language works237,207