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TIC: crecimiento sostenido

2007· article· en· W32126229 on OpenAlexfundno aff
Montse Fernández

Bibliographic record

VenueDatamation: la revista española de tecnología de la Información para empresa · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology in Education and Healthcare
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeography

Abstract

fetched live from OpenAlex

Anthropogenic activity has increased human exposure to metals and resulted in metal induced toxicity. Essential trace elements like cobalt (Co), nickel (Ni), and manganese (Mn) are best known for their roles as important cofactors in many enzymes involved in signalling, metabolism, and response to oxidative stress. However, deficiencies as well as long-term overexposure to these metals can result in negative health effects. Co has been associated with cardiomyopathy, lung disease, and hearing damage, while Ni is a known carcinogen, as well as a common sensitizing metal. Mn is best classified as a neurotoxicant that causes a disorder alike to idiopathic Parkinson's disease known as Manganism. Although the mechanisms of Co, Ni, and Mn toxicity are complex and have yet to be fully elucidated, research over the years has provided useful insights into understanding metal-induced detrimental effects at the cellular and molecular level. One area of research that has been explored in less detail are metal interactions with lipids and biological membranes, which are a potentially critical target as membranes are the first point of contact for cells. This review covers the current understandings of Co, Ni and Mn toxicity, in terms of human exposure, homeostasis and mechanisms of transport, potential cellular targets, and, of primary focus, metal interactions with lipid and biomembranes. A variety of effects like membrane rigidification, leakage affecting membrane potentials, lipid phase changes, alterations in lipid metabolism and changes of cellular morphology illustrate the vast potential for metal-based membrane effects contributing to their toxicity.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.407
Teacher spread0.383 · 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
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
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueDatamation: la revista española de tecnología de la Información para empresaSame topicTechnology in Education and HealthcareFrench-language works237,207