Evidence for a Multistep Model for Eukaryotic Polyamine Transport
Bibliographic record
Abstract
One of the most intriguing aspects of polyamine biology is the considerable diversity of their functions in the cell. The involvement of polyamines in such a multiplicity of parallel activities obviously requires mechanisms for the regulation of their concentrations in the various intracellular compartments. Much progress has been made in our understanding of polyamine homeostasis, and it is now clear that antizymes (AZ) are major players in regulating the size of cellular polyamine pools through the feedback inhibition exerted by these proteins on ornithine decarboxylase (ODC) activity and levels, and on polyamine uptake activity ( 1 ). However, our current view of how polyamines are distributed throughout the cytoplasm and nucleus after their synthesis is severely limited. The problem of polyamine microcompartmentalization is especially important in eukaryotic cells, where polyamines are expected to simultaneously act in the cytosol (e.g., in ribosomes), in close vicinity of the plasma membrane (e.g., ion channel gating), and in membrane-bound organelles (e.g., nucleus, mitochondria). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".