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Record W12269784 · doi:10.1139/y02-106

A Situated, Embodied and Dynamical Systems Approach to Understanding Learning and Memory.

2006· article· en· W12269784 on OpenAlexvenueno aff
Eduardo Izquierdo‐Torres, Inman Harvey

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsSituatedEmbodied cognitionSituated learningComputer scienceCognitive scienceSociologyArtificial intelligencePsychologyMathematics education

Abstract

fetched live from OpenAlex

The effect of tiron (4,5-dihydroxy-1,3-benzene disulfonic acid) on the binding of Ca2+ and its effect on vascular responses of the rat perfused mesenteric bed was studied at concentrations of tiron that are used widely to scavenge superoxide anion. In competition assays in buffered solutions with no tissue present, tiron decreased the fluorescence ratio of fura-FF, a measure of [Ca2+]: the inhibition constant (Ki) of tiron with Ca2+ was 0.692 +/- 0.036 mM. In the mesenteric bed perfused at constant flow and preconstricted with 90 mM KCl, tiron evoked decreases in perfusion pressure of the mesenteric bed in a concentration-dependent manner (Rmax = 43.58 +/- 2.6 mmHg; EC50 = 1.46 +/- 0.33 mM). This vasodilator effect of tiron was similar in the presence of the superoxide anion scavenger, tempol (Rmax = 46.12 +/- 1.87 mmHg; EC50 = 1.34 +/- 0.27 mM). In the presence of 90 mM KCl, increasing concentrations of Ca2+ increased perfusion pressure and tiron shifted the concentration-response curve to Ca2+ to the right. In freshly drawn blood from rats, tiron increased clotting time. The data indicate that tiron binds Ca2+ at concentrations at or below those commonly used to scavenge superoxide anion, an action that may be responsible for a variety of biological responses. The interpretation of effects of tiron in previous work on the role of superoxide anion may need to be re-evaluated.

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 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 score0.610
Threshold uncertainty score0.275

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 communication0.0000.000
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 head0.070
GPT teacher head0.336
Teacher spread0.266 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2006
Admission routes1
Has abstractyes

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