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Record W6884478549 · doi:10.1021/jp409543p.s001

Investigating\nWater Interactions with Collagen Using <sup>2</sup>H Multiple Quantum\nFiltered NMR Spectroscopy To Provide Insights\ninto the Source of Double Quantum Filtered Signal in Tissue

2016· article· en· W6884478549 on OpenAlexaff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCollagen: Extraction and Characterization
Canadian institutionsThe King's University
Fundersnot available
KeywordsNuclear magnetic resonance spectroscopyJ-couplingRelaxation (psychology)Coupling constantCoupling (piping)SIGNAL (programming language)Transverse relaxation-optimized spectroscopyMoleculeSpectroscopy

Abstract

fetched live from OpenAlex

In\nan effort to provide insight into the molecular origins of the <sup>2</sup>H double quantum filtered (DQF) NMR signal observed in connective\ntissue, specifically spinal disc tissue, <sup>2</sup>H multiple quantum\nfiltered (MQF) NMR spectroscopy is used to study the structure and\ndynamics of D<sub>2</sub>O in collagen as a function of hydration.\nResidual quadrupolar coupling constants are measured and decrease\nfrom 3500 to 20 Hz while <i>T</i><sub>2</sub> relaxation\ntimes increase from 0.65 to 20 ms as hydration increases. Analysis\nof the data indicates that the quadrupolar coupling and <i>T</i><sub>2</sub> relaxation arises when water molecules spend time in\nrestricted environments. The residual quadrupolar coupling is influenced\nalmost exclusively by the most restricted water sites, the clefts\nof the triple helices not exposed on the surface of the fibrils, while\nthe <i>T</i><sub>2</sub> relaxation has secondary contributions\nfrom less restricted water environments. The magnitudes of the measured\nvalues are consistent with results from DQF NMR studies of spinal\ndisc tissue, supporting the assertion that water binding to collagen\nis a major contributor to the DQF NMR signal observed in spinal disc\ntissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.001

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 head0.049
GPT teacher head0.278
Teacher spread0.229 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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