Seeing the fetus from a DOHaD perspective: discussion paper from the advanced imaging techniques of DOHaD applications workshop held at the 2019 DOHaD World Congress
Bibliographic record
Abstract
Advanced imaging techniques are enhancing research capacity focussed on the developmental origins of adult health and disease (DOHaD) hypothesis, and consequently increasing awareness of future health risks across various subareas of DOHaD research themes. Understanding how these advanced imaging techniques in animal models and human population studies can be both additively and synergistically used alongside traditional techniques in DOHaD-focussed laboratories is therefore of great interest. Global experts in advanced imaging techniques congregated at the advanced imaging workshop at the 2019 DOHaD World Congress in Melbourne, Australia. This review summarizes the presentations of new imaging modalities and novel applications to DOHaD research and discussions had by DOHaD researchers that are currently utilizing advanced imaging techniques including MRI, hyperpolarized MRI, ultrasound, and synchrotron-based techniques to aid their DOHaD research focus.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".