PowerCHORD: constructing optimal experimental designs for biological rhythm discovery
Bibliographic record
Abstract
Abstract Equally spaced temporal sampling is the standard protocol for the study of biological rhythms. These equispaced designs perform well when calibrated to an oscillator’s period yet can have systematic detection biases when applied to rhythms of unknown periodicity. Here, we present a broadly-applicable set of computational methods for seeking optimal measurement schedules for rhythm detection. Our PowerCHORD methods generate experimental designs by maximizing a closed-form expression for the statistical power of the cosinor model using a black-box optimization method (differential evolution), a brute-force search, or mixed-integer conic programming. Application of these three methods showed numerically that they improve upon equispaced designs under many experimental contexts. Our numerical results also revealed an intuitive approach for achieving optimal power for simultaneous investigation of circadian, circalunar, and circannual rhythms. Our findings suggest that timing optimization is an effective yet under-explored tool for improving biological rhythm discovery.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".