data and script for Banousse et al: <b>Genetic and environmental basis of transcriptional thermal plasticity of brook charr fry</b>
Bibliographic record
Abstract
DB_Brain_chip_Laval_calculation.xlsx: This file contains the calculation of N0 (following Tuomi et al. 2010) and Dct (DN0) derived from the Crt, which is the fractional PCR cycle number where fluorescence crosses the quantification threshold.DB_dct_Brain_chip_Laval_Box_Cox.csv: This file includes the dct transformed values (using Box-Cox transformation) for each of the 10 genes (AVT1, BDNF, CART, CCK.L, CIRBPA, DCX, NEUROD1, NPY, PCNA, SOX2).Code_LMM_Brain_chip_Laval.Rmd: This script contains the code used to assess the Linear Mixed Models (LMM) for each of the 10 genes analyzed.data_for_ASREML.zip: This zip file contains the same data as DB_dct_Brain_chip_Laval_Box_Cox.csv, but it is divided based on the parental thermal regimes (cold and warm). Additionally, it includes the pedigree matrix. These files are used in the (ASREML_Brain_chip_Laval.Rmd) to perform quantitative genetic analysis and partition the variance in gene expression into genetic, environmental, and parental components. ASREML_Brain_chip_Laval.Rmd: the code script used for the quantitative genetic analysisdata_for_G X E _analysis.zip : This zip file contains the data used in the GxE_Analysis_Brain_chip_Laval.Rmd script to assess the genotype-by-environment analysis. It includes data exclusively for full-sib families, treated as a single genotype.G X E Analysis_Brain_chip_Lval.Rmd: This script contains the code used to perform the genotype-by-environment (GxE) analysis.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.350 |
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".