Tube_Bioassay_Power_Simulation_Calculator
Bibliographic record
Abstract
The purpose of this project is to develop a sample size framework for investigating differences between insecticide treatments in WHO Tube assays, with a particular focus on PBO synergism. While typically sample sizes are determined prior to a study to reliably detect a given effect size, current WHO guidance fixes the sample size of this synergism assay to four tubes of each treatment (‘4x4’). This pre-determined sample size limits the effect size that can be reliably detected. Consequently, experimental setups may not be powered to detect smaller differences in mortality between treatments and is at risk of exaggerating the magnitude of comparisons that are found to be significant (the lesser known risk of underpowered studies). A better understanding of the power of this 4x4 setup to detect differences between treatments is needed. General rules-of-thumb are required for what effect sizes can be reliably interpreted as a true effect, ideally in the form of a ‘threshold’ where only a mortality difference larger than a pre-defined amount is considered indicative of synergism. Additionally, while it would be ideal if all bioassays were conducted simultaneously on the same day, in practice assays may be spread over multiple days thus introducing a between-day variation which must be accounted when assessing power and setting appropriate synergism thresholds. To identify a suitable threshold for synergism requires a power analysis in reverse, where the probability of detecting a range of effect sizes is quantified across different hypothetical experimental designs (i.e. sample sizes). However, power analysis requires assumptions about variance between replicates (here the separate tubes for each treatment). Given reasonable assumptions about variance, the minimum difference in mortality that can be reliably detected can be outlined for a standard 4x4 Tube assay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".