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Record W2189254605 · doi:10.3324/haematol.2015.126870

Mice are poor heme absorbers and do not require intestinal Hmox1 for dietary heme iron assimilation

2015· letter· en· W2189254605 on OpenAlexafffund
Carine Fillebeen, Konstantinos Gkouvatsos, Gabriela Fragoso, Annie Calvé, Daniel Garcia‐Santos, M. Buffler, Christiane Becker, K. Schümann, Prem Ponka, Manuela M. Santos, Kostas Pantopoulos

Bibliographic record

VenueHaematologica · 2015
Typeletter
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsHemoglobinHemeAnemiaMyoglobinIron deficiencyChemistryIron-deficiency anemiaFood scienceBiochemistryPhysiologyInternal medicineBiologyMedicine

Abstract

fetched live from OpenAlex

Dietary iron absorption offsets non-specific iron losses and is crucial for systemic iron balance. Inadequate iron acquisition leads to iron deficiency, a condition associated with anemia, poor pregnancy outcome and impaired cognitive and motor development.1 Heme derived from hemoglobin (Hb) and myoglobin of meat products is an important nutritional iron source.2 It is more bioavailable compared to inorganic iron, although less abundant in mixed diets. Thus, two-thirds of body iron stores are estimated to originate from heme, which accounts for only one-third of total dietary iron content in Western populations.3

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.062
GPT teacher head0.299
Teacher spread0.237 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations47
Published2015
Admission routes2
Has abstractyes

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