Bibliographic record
Abstract
We have long appreciated that the controlled movement of ions and solutes across the cell surface or plasma membrane affects every aspect of cell function, ranging from membrane excitability to metabolism to secretion, and is also critical for the long-term maintenance of cell viability. Studies examining these physiological transport processes have revealed a vast array of ion channels, transporters and ATPase-driven pumps that underlie these transmembrane ionic movements and how acquired or genetic disruption of these processes are linked to disease. More recently, it has become evident that the ongoing function of intracellular organelles and subcellular compartments also depends heavily on the controlled movement of ions to establish distinct pH or ionic environments. However, limited experimental access to these subcellular domains/structures has hampered scientific progress in this area, due in large part to the difficulty of applying proven functional assays, such as patch clamp and radiotracer methodologies, to these specialized membrane locations. Using both functional and immune-labeling assays, we now know that the types and complement of channels, transporters and pumps located within intracellular membranes and organelles often differ from those present on the plasma membrane. Moreover, it appears that this differential distribution is due to the presence of discrete tags/signals present within these transport proteins that dictate their sorting/trafficking to spatially discrete membrane compartments, where they may also interact with scaffolding proteins that help maintain their localization. Such targeting signals may thus operate in a manner analogous to the way a postal code is used to direct the delivery of a letter.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".