Bibliographic record
Abstract
Problem: Evaluating very soft foetal brains is problematic, since anatomic information is often lost when these collapse on a dissection board. Methods: Present cases of very soft foetal brains photographed under water, discuss technical details on this technique, and indicate how these data can be used to evaluate the brains. Results: Foetal brains from intrauterine foetal deaths and from foetal terminations that have a long death-to-delivery time are often very soft, even after fixation, and collapse under their own weight on a dissection board. To better evaluate these brains, they have been floated and photographed in water. When possible, the brain is photographed intact in ventral and dorsal views. After the brainstem with cerebellum is removed and hemispheres are separated, these are all photographed; hemispheres are imaged in both lateral and medial views. This technique records developmental data about cortical gyration, the presence of olfactory tracts/bulbs, corpus callosum posterior extension, cerebellum foliation, and brainstem, which can be compared to standard brain development references. Problems with this technique include fragmentation of autolyzed brain into water. Discussions: Photography of very soft foetal brains under water allows evaluation of brains that normally collapse under their own weight. In cases too soft for meaningful dissection, these data often provide the only available brain developmental information. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Photograph foetal brain under water 2. Evaluate key aspects of external examination using standard developmental literature
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".