Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are expressed in a variety of cold-hardy organisms to prevent or slow internal ice growth. AFPs bind to specific planes of ice through their ice-binding surfaces. Fluorescence-based ice plane affinity (FIPA) analysis is a modified technique used to determine the ice planes to which the AFPs bind. FIPA is based on the original ice-etching method for determining AFP-bound ice-planes. It produces clearer images in a shortened experimental time. In FIPA analysis, AFPs are fluorescently labeled with a chimeric tag or a covalent dye then slowly incorporated into a macroscopic single ice crystal, which has been preformed into a hemisphere and oriented to determine the a- and c-axes. The AFP-bound ice hemisphere is imaged under UV light to visualize AFP-bound planes using filters to block out nonspecific light. Fluorescent labeling of the AFPs allows real-time monitoring of AFP adsorption into ice. The labels have been found not to influence the planes to which AFPs bind. FIPA analysis also introduces the option to bind more than one differently tagged AFP on the same single ice crystal to help differentiate their binding planes. These applications of FIPA are helping to advance our understanding of how AFPs bind to ice to halt its growth and why many AFP-producing organisms express multiple AFP isoforms.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".