Bibliographic record
Abstract
Recent improvements in confocal technology permit the use of a confocal microscope as an effective tool to both measure concentration and visualize distribution of several ions. Effective discrimination against out-of-focus information in the confocal microscope permits for mapping of ion distribution at the subcellular level ( 1 ). To generate such a map with any degree of accuracy it is necessary to compensate for inherent cellular inhomogeneities in optical path, probe distribution, etc. To do so ratiometric measurements are usually employed ( 2 , 3 ). Fluorescence ratiometry takes advantage of a differential spectral sensitivity of a ratio probe to the measured parameter (e.g., ion concentration). Whereas fluorescent properties of the ratio probe at one wavelength are parameter sensitive in one manner, the emission (or excitation) of the probe at a distinct, well-separated wavelength must be either parameter insensitive, inversely sensitive, or exhibit a different sensitivity profile. Emission intensities for the two wavelengths are then divided by each other and thus the resulting ratio becomes normalized for inhomogeneities in probe distribution and concentration and in the system geometry. To obtain meaningful data the ratio values must be calibrated against a standard, which usually is a graded change in the parameter. Modern day confocal microscopy depends on lasers as light sources which causes a severe limitation in number of wavelengths available for fluorochrome excitation. Several H + -sensitive fluorochromes are excited at wavelengths close to those emitted by standard lasers, however, this is not the case with Ca 2+ -sensitive fluorochromes ( 4 ). 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".