Antifreeze Proteins of Teleost Fishes
Bibliographic record
Abstract
Marine teleosts at high latitudes can encounter ice-laden seawater that is approximately 1 degrees C colder than the colligative freezing point of their body fluids. They avoid freezing by producing small antifreeze proteins (AFPs) that adsorb to ice and halt its growth, thereby producing an additional non-colligative lowering of the freezing point. AFPs are typically secreted by the liver into the blood. Recently, however, it has become clear that AFP isoforms are produced in the epidermis (skin, scales, fin, and gills) and may serve as a first line of defense against ice propagation into the fish. The basis for the adsorption of AFPs to ice is something of a mystery and is complicated by the extreme structural diversity of the five antifreeze types. Despite the recent acquisition of several AFP three-dimensional structures and the definition of their ice-binding sites by mutagenesis, no common ice-binding motif or even theme is apparent except that surface-surface complementarity is important for binding. The remarkable diversity of antifreeze types and their seemingly haphazard phylogenetic distribution suggest that these proteins might have evolved recently in response to sea level glaciation occurring just 1-2 million years ago in the northern hemisphere and 10-30 million years ago around Antarctica. Not surprisingly, the expression of AFP genes from different origins can also be quite dissimilar. The most intensively studied system is that of the winter flounder, which has a built-in annual cycle of antifreeze expression controlled by growth hormone (GH) release from the pituitary in tune with seasonal cues. The signal transduction pathway, transcription factors, and promoter elements involved in this process are just beginning to be characterized.
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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".