Radiographic Imaging Application in Capturing Nest Escaping Movement by Sea Turtle Hatchlings
Bibliographic record
Abstract
Nest escaping is a vital process that takes up a significant amount of energy and time during the early life of sea turtle hatchlings. Previous studies have shown how hatchlings benefit from this aggregation behaviour while digging upward in the nested column. However, there is a lack of information on the relationship between the dimensions of nest escaping movement formation. This study explores the potential of radiographic imaging in capturing the dimension of digging formation to relate its potential with the energy conservation mechanism. This article describes the challenges and prospects of capturing hatchlings’ movement while digging up and escaping their underground nest via radiographic imaging (X-ray). Several trials have been conducted to seek the most suitable approach to be applied as a standard method for future studies. We developed an open-respirometry chamber with a supply of oxygen for egg incubation according to preferred clutch sizes to meet one of the objectives, which is to determine the effect of clutch sizes on the dimension of digging formation. Exposure usage of 125 kV, 40.0 mAs, and 62.5 ms were determined to produce significant X-ray images. Additionally, iron flux has been determined to be useful in measuring hatchlings’ digging progress, as it has the potential to enhance our image quality and observation. We were able to observe the position of the hatchlings with such development. Nevertheless, we do not know the accurate dimension of digging movement formation as modelled by the hatchlings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".