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Record W2044373204 · doi:10.5539/mas.v2n3p64

Alteration of Iron Metabolism of Elite Female Distance Runners in Intensity Training

2008· article· en· W2044373204 on OpenAlexvenueno aff
Bayar Tsinggel, Bao Dagula

Bibliographic record

VenueModern Applied Science · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Physical Performance
Canadian institutionsnot available
Fundersnot available
KeywordsSoluble transferrin receptorHemoglobinIron deficiencyFerritinTransferrinErythropoietinSerum ironInternal medicineTransferrin saturationEndocrinologyTotal iron-binding capacityTransferrin receptorChemistryIron statusMedicineAnimal scienceAnemiaBiology

Abstract

fetched live from OpenAlex

The hemoglobin (Hb), serum Iron (SI), serum ferritin, serum transferrin (Tr), serum transferrin receptor (sTfR) concentration, Erythrocyte hemoglobin distribution width(RDW) and erythropoietin(EPO) in adults are suggested to provide a sensitive measure of iron depletion and the serum ferritin (Ferr) concentration is able to indicate the entire range of iron status, from iron deficiency to iron overload. However, little is known about those indexes in elite female distant runners. The objective of this study was to determine the above indexes in intensity training of 8 elite female distant runners two months ahead of national competition. Result showed that Hb concentration decreased in second sampling point and then recovered; SI concentration firstly decreased then increased, but it was not significant; Tr concentration, RDW and EPO level increased in second sampling point then fell to baseline; there was no significant difference in Ferr and sTfR concentration. It can be concluded that due to intensity training, iron metabolism in the initial stage of training was disturbed and then modulated in following training phase.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.238
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

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