Bibliographic record
Abstract
The discovery of radically different antifreeze proteins (AFPs) in fishes during the 1970s and 1980s suggested that these proteins had recently and independently evolved to protect teleosts from freezing in icy seawater. Early forays into the isolation and characterization of AFP genes in these fish showed they were massively amplified, often in long tandem repeats. The work of many labs in the 1980s onward led to the discovery and characterization of AFPs in other kingdoms, such as insects, plants, and many different microorganisms. The distinct ice-binding property that these ice-binding proteins (IBPs) share has facilitated their purification through adsorption to ice, and the ability to produce recombinant versions of IBPs has enabled their structural characterization and the mapping of their ice-binding sites (IBSs) using site-directed mutagenesis. One hypothesis for their ice affinity is that the IBS organizes surface waters into an ice-like pattern that freezes the protein onto ice. With access now to a rapidly expanding database of genomic sequences, it has been possible to trace the origins of some fish AFPs through the process of gene duplication and divergence, and to even show the horizontal transfer of an AFP gene from one species to another.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".