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Madrid entre bombas, muertes y lágrimas (11 de Marzo de 2004)

2004· article· en· W3556679 on OpenAlexaboutno aff
Herminio de la Red Vega

Bibliographic record

VenueReligión y cultura · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Culture and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Twenty-four hour duplicate diets, including drinking water and snacks, were collected from 24 adults living in five Canadian cities. Each diet was separated by the participants into 10 food categories, and each of the samples was analyzed in duplicate for lead, cadmium, arsenic and fluoride. Minimum detection limits for the respective elements in foods were about 0.1, 0.01, 0.3 and 5 ng/g. Mean dietary intakes were 53.8 micrograms/day or 0.80 micrograms/kg/day for lead, 13.8 micrograms/day or 0.21 micrograms/kg/day for cadmium, and 16.7 micrograms/day or 0.26 micrograms/kg/day for arsenic. The median intakes were 42.7 micrograms/day or 0.57 mu/kg/day for lead, 11.9 micrograms/day or 0.17 micrograms/kg/day for cadmium, and 9.79 micrograms/day or 0.139 micrograms/kg/day for arsenic. Half of the participants lived in communities with 1 microgram/g fluoride in the drinking water, and half lived in cities with less than 0.2 microgram/g fluoride in the water. The dietary intake of fluoride for the former was 2802 micrograms/day or 39.7 micrograms/kg/day; while that of the latter was 563 micrograms/day or 8.5 micrograms/kg/day. The respective median intakes of fluoride were 2090 micrograms/day or 30.3 micrograms/kg/day, and 414 micrograms/day or 7.0 micrograms/kg/day. Contribution of individual foods and food categories to the dietary intakes is discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.212
Teacher spread0.201 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2004
Admission routes1
Has abstractyes

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