Modelling skin surface areas involved in water transfer in the Palmate Newt (<i>Lissotriton</i> <i>helveticus</i>)
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) based 3D reconstructions were used to derive accurate quantitative data on body volume and functional skin surface areas involved in water transfer in the Palmate Newt (Lissotriton helveticus (Razoumovsky, 1789)). Body surface area can be functionally divided into evaporative surface area that interacts with the atmosphere and controls the transepidermal evaporative water loss (TEWL); ventral surface area in contact with the substratum that controls transepidermal water absorption (TWA); and skin surface area in contact with other skin surfaces when amphibians adopt water-conserving postures. We generated 3D geometries of the newts via volume-rendering by a “segmentation” process carried out using a graph-cuts algorithm and a Web-based interface. The geometries reproduced the two postures adopted by the newts, i.e., an I-shaped posture characterized by a straight body without tail coiling and an S-shaped posture where the body is huddled up with the tail coiling along it. As a guide to the quality of the surface area estimations, we compared measurements of TEWL rates between living newts and their agar replicas (reproducing their two postures) at 20 °C and 60% relative humidity. Whereas the newts did not show any physiological adaptations to restrain evaporation, they expressed an efficient S-shaped posture with a resulting water economy of 22.9%, which is very close to the 23.6% reduction in evaporative surface area measured using 3D analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".