A system for quantitative evaluation of fixatives for light microscopy using paraffin sections of kidney and brain
Bibliographic record
Abstract
A numerical scoring system is presented for evaluating structural fixation of certain mammalian tissues for light microscopy. Small pieces of rat's kidney and brain, tissues for which artifacts of fixation are well documented, were fixed in various fluids. Random code numbers hid their identities, and paraffin sections were stained to show nuclear chromatin, cytoplasm and extracellular materials. Two sections of each specimen were examined and awarded scores according to described schemes, for microanatomical and cytological fixation. The assessment was confined to preservation of structure; chemical changes were not taken into account. When the code was broken, the scores for both sections of each specimen from each fixative were added. The scores obtained (24 best to eight worst) are generally comparable to the grades (I-V) given for traditional fixatives by JR Baker. The criteria for quality of fixation were defined more explicitly than those for Baker's grading method, which used mouse testis as the test object. Assessments are presented for several traditional fixatives and for four zinc-formaldehyde mixtures. The scoring system should be useful for evaluating newly developed fixatives for animal tissues for light microscopic examination of paraffin sections. In an evaluation of four traditional fixatives and four zinc-formaldehyde mixtures, variation among three different observers was only +/-1 point on either side of the median score for microanatomical or cytological preservation by any of the eight fixatives. This approach has certain limitations, notably that the criteria for cytological fixation do not include the preservation of chromosomes or specific cytoplasmic organelles.
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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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".