thierrygosselin/stackr: v.0.4.6 `write_colony` replace `haplo2colony` and `stackr` now likes PCs!
Bibliographic record
Abstract
v.0.4.6 I'm pleased to announce that stackr parallel mode now works with Windows! Nothing to install, just need to choose the number of CPU, the rest is done automatically. haplo2colony is deprecated. Use the new function called write_colony! write_colony: works similarly to the deprecated function haplo2colony,* with the major advantage that it's no longer restricted to STACKS haplotypes file. * The function is using the `tidy_genomic_data` module to import files. So you can choose one of the 10 input file formats supported by `stackr`! * other benefits also include the possibility to efficiently test MAF, snp.ld, haplotypes/snp approach, whitelist of markes, blacklist of individuals, blacklist of genotypes, etc. with the buit-it arguments. * the function only **keeps markers in common** between populations/groups and **is removing monomorphic markers**. * **Note:** there are several *defaults* in the function and it's a complicated file format, so make sure to read the function documentation, please, and `COLONY` manual.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.541 | 0.649 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".