Commentary: Brightness of the Night Sky Affects Loggerhead (Caretta caretta) Sea Turtle Hatchling Misorientation but Not Nest Site Selection
Bibliographic record
Abstract
As research in the field of artificial light at night has increased and more sophisticated quantification of the influence of light is required, ecologists have been seeking a method to quickly and cost-effectively measure artificial light at night. Measuring light at night in a biologically meaningful way is complicated, and requires specialist knowledge (Swaddle et al., 2015; Hänel et al., 2018). It is therefore potentially tempting to adopt tools developed for other disciplines, such as the low-cost and easy-to-use radiance meter “Sky Quality Meter” (SQM), and the SQM-L model, which have both been widely used in night sky brightness studies. Unfortunately, in some cases these instruments are being used for purposes beyond which they were intended. Stanley et al. (2020) used SQMs to make observations of radiance from a series of points on the sky dome, with the intention of thereby measuring hemispherical light levels, to investigate their influence on sea turtle nesting location and hatchling behavior. We appreciate their intention to measure angularly resolved radiance, their analysis and conclusions, and do not disagree with their ultimate recommendations. However, we are concerned that the radiance data are unreliable because of an inadvisable use of an SQM. For this reason, we recommend that researchers do not use SQMs to measure light from very heterogeneous scenes (including the horizon) for biological and ecological studies, because those data could be difficult to reproduce and interpret, and therefore open to challenge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".