Antifreeze Protein-induced Morphological Modification Mechanisms Linked to Ice Binding Surface
Bibliographic record
Abstract
The mechanisms by which the antifreeze protein (AFP) modifies the ice morphology are identified precisely as surface poisoning by the ice binding surface (IBS) of insect AFPs and as bridge-induced surface reconstruction by the IBS of fish AFPs and antifreeze glycoproteins. The primary surfaces of hexagonal ice have predetermined face indices. The "two-dimensional" insect type IBS has regularly spaced binding intervals in two directions. It causes surface poisoning by matching and reinforcing simultaneously intersecting strong bonding directions on the primary ice surfaces. The secondary ice surfaces have variable face indices. The "one-dimensional" and "irregular" IBS variants of fish AFPs and antifreeze glycoproteins are either linearly extended with regular ice binding intervals or have ice binding sites lacking spacing regularity. These variants can bridge transversely lattice periods or shorter oxygen-oxygen distances between parallel adjacent strong bonding directions that do not intersect. Thus, one-dimensional and irregular IBS variants induce supplementary bridges cross-wise on selected secondary surfaces by mimicking strong bonding directions that are not present in the ice structure. These proteins cause surfaces with variable face indices, which in the absence of the AFPs would not grow flat, to appear in the morphology. Whereas for the primary ice surfaces it is only the morphological importance that is determined by the experimental conditions, for the secondary ice surfaces it is the face indices themselves that become adjusted in the process of maximizing the AFP-substrate interaction through attainment of the best structural match. The growth morphology of the AFP-ice system is derived from various factors, including the face indices, surface molecular compositions, relative growth rates, and the mechanisms responsible for that morphology. The theoretical formulation agrees with experiments over a wide range and resolves these, to date, unexplained phenomena.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".