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Record W2114018706 · doi:10.1039/c1cs15099e

Ionic conductance of synthetic channels: analysis, lessons, and recommendations

2011· review· en· W2114018706 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Society Reviews · 2011
Typereview
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsConductanceSalientIonic bondingChannel (broadcasting)IonIon channelRange (aeronautics)Chemical physicsNanotechnologyChemistryComputer scienceBiological systemMaterials sciencePhysicsArtificial intelligenceTelecommunicationsCondensed matter physicsBiology

Abstract

fetched live from OpenAlex

Synthetic ion channels have been known for nearly three decades, but it is only in the past decade that analysis of the currents these ionic conductors carry has become a standard technique. A broad range of structural types have been explored and these reports have produced a very diverse collection of ion channel conductance behaviours. In this critical review we describe a notational method to extract salient information from reported ion channel experiments. We use an activity grid to represent quantitative information on conductance and opening duration with a five-level colour code to represent qualitative information on the nature of the conductance-time profile. Analysis of the cumulative dataset suggests that the reported conductance data can reflect the structural features of the compounds prepared, but does also reflect the energetic landscape of the bilayer membrane in which synthetic ion channels function (143 references).

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.

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 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: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.347
Teacher spread0.247 · 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