Bibliographic record
Abstract
Abstract p H is a profound regulator of cellular function. It is, therefore, often important to assess intracellular p H . Given the small size of individual cells and their sensitivity to perturbation, measurement of intracellular p H requires sensitive, indirect measurement approaches. These include equilibration of weak acids/bases, nuclear magnetic resonance spectroscopy, p H microelectrodes, fluorescent p H indicator dyes and p H ‐sensitive fluorescent proteins. Presently, the use of fluorescence techniques predominates, as these permit sensitive detection and the possibility to discretely measure p H in different cellular compartments. The selection of intracellular p H measurement technique is guided by consideration of their strengths and weaknesses, in addition to technical considerations. Cells resist changes of p H through p H buffering molecules, including proteins and bicarbonate, which together are called the cell's buffer capacity. Key Concepts Cellular processes are highly sensitive to pH, so cells have redundant mechanisms to control their pH. Buffer capacity is the ability of cells to control pH by absorbing or releasing H + from chemical pH buffering molecules. Cell membranes contain embedded transport proteins able to move H + , or pH‐buffering HCO 3 − in order to tightly control cytosolic and organellar pH. pH of the cytosol and other intracellular compartments can be measured. Certain molecules will absorb light and release a photon of light at a longer wavelength, a process known as fluorescence. Fluorescent dyes and proteins are the most common means to report on intracellular pH.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".