Electroanalysis of Modes of Methylene Blue Binding to Gold-Tethered DNA
Bibliographic record
Abstract
Electrochemistry and electroanalysis of drugs, bioactive compounds, genotoxic and ecotoxic substances novel electrochemical biosensors and their applications application of new electrode materials combination of electrochemistry with other techniques (e.g., spectral methods) Structure-reactivity relationship in redox-active molecular systems redox mechanistic studies of organic and coordination compounds bond activation by electron transfer correlation of experimental data with theoretical calculations and other related problems electron transfer in molecules with multiple redox centers Biopolymer electrochemistry electrochemical properties and analytical use of natural and synthetic nucleic acids and redox-labelled nucleic acid conjugates protein electrochemistry and electroanalysis: structure and interaction effects electrochemistry of natural and chemically modified carbohydrates Biological membranes, their mimics and transporting processes transporting processes across membranes transport through nanopores Novel materials and nanotechnology for electrochemical sensors electrode materials and surface modifications nanostructured surfaces nanoobject for biopolymer labeling Program
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".