Transcriptome dynamics over a lunar month in a broadcast spawning acroporid coral
Bibliographic record
Abstract
On one night per year, at a specific point in the lunar cycle, one of the most extraordinary reproductive events on the planet unfolds as hundreds of millions of broadcast spawning corals release their trillions of gametes into the waters of the tropical seas. Each species spawns on a specific night within the lunar cycle, typically from full moon to third quarter moon, and in a specific time window after sunset. This accuracy is essential to achieve efficient fertilization in the vastness of the oceans. In this report, we use transcriptome sequencing at noon and midnight across an entire lunar cycle to explore how acroporid corals interpret lunar signals. The data were interrogated by both time-of-day-dependent and time-of-day-independent methods to identify different types of lunar cycles. Time-of-day methods found that genes associated with biological clocks and circadian processes change their diurnal cycles over the course of a synodic lunar cycle. Some genes have large differences between day and night at some lunar phases, but little or no diurnal differences at other phases. Many clock genes display an oscillation pattern indicative of phase shifts linked to the lunar cycle. Time-independent methods found that signal transduction, protein secretion and modification, cell cycle and ion transport change over the lunar timescale and peak at various phases of the moon. Together these data provide unique insights into how the moon impinges on coral transcription cycles and how lunar light may regulate circalunar timing systems and coral biology.
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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 | 0.000 | 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".