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Record W1992004925 · doi:10.1016/s1728-869x(10)60004-4

Meal Composition and Iron Status of Experienced Male and Female Distance Runners

2010· article· en· W1992004925 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Exercise Science & Fitness · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of SaskatchewanWestern University
Fundersnot available
KeywordsIron statusSerum ferritinHemoglobinMealFerritinIron supplementSerum ironComposition (language)Animal scienceMedicineChemistryAnemiaInternal medicinePhysiologyIron deficiencyBiology

Abstract

fetched live from OpenAlex

This study compared the iron status of middle-distance runners consuming meals providing low-medium iron availability (LMIA) or medium-high iron availability (MHIA), and determined the effect of a 4-week intervention on iron status in LMIA participants. Seventeen university-aged competitive runners and eight inactive controls participated. Mean serum ferritin levels were significantly greater in the MHIA group (58.7 ± 9.7ng·mL−1) than in the LMIA group (43.6 ± 10.9 ng·mL−1). Significant (p < 0.05) correlations were noted between absorbable dietary iron and serum iron (r = 0.639), total iron binding capacity (r = −0.636) and hemoglobin (r = 0.523). The mean absorbable dietary iron was significantly greater following the intervention in LMIA males (Test 1, 0.97 ± 0.3 mg·day−1; Test 2, 1.54 ± 0.5 mg·day−1; p < 0.05). Dietary advice did not improve iron status. These data suggest that meal composition may influence the amount of iron available for absorption and for maintaining iron status over time.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.261
Teacher spread0.254 · 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