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Record W6930641680 · doi:10.5281/zenodo.12700082

Tube_Bioassay_Power_Simulation_Calculator

2024· other· en· W6930641680 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typeother
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsImpact
Fundersnot available
KeywordsSample size determinationStatistical powerSample (material)Variance (accounting)Range (aeronautics)Power (physics)Multiple comparisons problem

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.410
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0020.000
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.037

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.

Opus teacher head0.031
GPT teacher head0.272
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it