Bibliographic record
Abstract
Provided here: -Scripts for Performing quantitative Fast FLIM FRET (qF3) Analysis. Organized into folders by steps (1-13) to run for each biological replicate (Step1_Replicate_Analysis) and then for all combined replicates (Step2_CombineReps_Analysis). -script used to calculate G factor (for which our original data can be provided upon request) -Example Master platemap. -LICENSE file for all code here. -Author: Nehad Hirmiz (Nehad.Hirmiz@gmail.com) Instructions: -Download all files here and extract .Zip. -Install MATLAB Version R2020a with toolboxes: Signal Processing, Curve Fitting, Image Processing. -IFF starting with INO FLIM Hyperspectral data * then contact lead for INO software package including (Release_r10357 package): INO FHS Acquisition, INO_FHS_Analysis, INO_FHS_Batch Analysis -Follow instructions to run these codes: See associated text at Protocol Exchange: Title: “Automated, quantitative Fast FLIM-FRET (qF3): A step-by-step protocol to measure dissociation constants for protein-protein interactions in live-cell screening applications.” LINK https://doi.org/10.21203/rs.3.pex-1354/v1 And Instructional videos: LINK https://www.youtube.com/playlist?list=PLUiSJrzzg9voe5sjA57oIbfOLAGIrHXRc
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.510 | 0.379 |
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".