thierrygosselin/stackr: v.0.4.6 `write_colony` replace `haplo2colony` and `stackr` now likes PCs!
Bibliographic record
Abstract
<strong>v.0.4.6</strong> I'm pleased to announce that <code>stackr</code> parallel mode now works with <strong>Windows</strong>! Nothing to install, just need to choose the number of CPU, the rest is done automatically. <code>haplo2colony</code> is deprecated. Use the new function called <code>write_colony</code>! <code>write_colony</code>: works similarly to the deprecated function <code>haplo2colony</code>,<code>* 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. </code>
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".