Bibliographic record
Abstract
Note: This changelog was automatically generated from the git log. New functionality Added cell2location to the spatial_decomposition task. Added nearest-neighbor ranking matrix computation to _utils. Datasets now store nearest-neighbor ranking matrix in adata.obsm["X_ranking"]. Added support for parsing Nextflow output and generating benchmark results for the website. Added max_samples parameter to qlocal, qglobal, qnn_auc, lcmc, qnn, and continuity metrics to allow for subsampling of data for faster computation. Added new scArches based methods: scarches_scanvi_xgb_all_genes and scarches_scanvi_xgb_hvg. Added prediction_method parameter to _scanvi_scarches to specify prediction method. Added _pred_xgb function to perform XGBoost prediction based on latent representations. Added obsm parameter to _xgboost function to allow specifying the embedding space for XGBoost training. Major changes Updated scvi-tools to version 0.20 in both Python and R environments. Updated datasets to include nearest-neighbor ranking matrix. Modified dimensionality reduction task to include nearest-neighbor ranking matrix computation in dataset generation. The website update workflow was refactored to use a new workflow using json instead of markdown. Updated the website generation process to remove duplicate BibTex entries. Added a new parse_metadata.py script for generating metadata for the website. Added a new function to openproblems.utils.py to get the member ID of a task, dataset, method or metric. Removed the redundant computation and storage of the nearest-neighbor ranking matrix in datasets. Minor changes Updated method names to be shorter and more consistent across tasks. Improved method summaries for clarity. Updated JAX and JAXlib versions to 0.4.6. Updated dependencies to support new versions of Snakemake and GitPython. Removed code related to "nbt2022-reproducibility" repo and merged it into the main website. Updated the schema for benchmark results to include submission time, code version, and resource usage metrics. Improved error handling and added logging to the parsing script. Removed the "raw.json" file from the results directory and merged all data into a single "results.json" file. Updated the workflow to upload the final results to the website's results directory instead of the data directory. Removed unnecessary code and refactored the parsing script for better readability. Added unit tests for the new parsing script. Updated the run_tests workflow to skip testing on the test_website branch. Updated the run_tests workflow to skip testing on the test_process branch. Updated the create-pull-request step to set the author for the pull request. Updated the run_tests workflow to skip testing on pull request reviews. Updated the update_website_content workflow to update the website on the main branch. Updated the main.bib file to fix a typo. Removed extraneous headings from task README files. Updated generate_test_matrix.py to use the new openproblems.utils.get_member_id function. Updated the website generation process to copy BibTex files to the correct location. Updated the process_requires section in setup.py to include gitpython. Updated git commit hash generation for openproblems functions. Modified _xgboost to allow for specifying tree_method. Modified _scanvi_scarches to consistently use unlabeled_category. Modified _scanvi_scarches to remove unnecessary copying of labels. Removed _scanvi_scarches functions that were redundant with _scanvi_scarches. Removed unused _scanvi functions. Modified _scanvi_scarches to allow for specifying prediction_method and handle unlabeled_category consistently. Documentation Improved the documentation of the auprc metric. Improved the documentation of the cell2location methods. Document sub-stub task behaviour Bug fixes Fixed an error in neuralee_default where the subsample_genes argument could be too small. Fixed an error in knn_naive where the is_baseline argument was set to False. Fixed calculation of ranking matrix in _utils to include ties. Fixed a bug in load_tenx_5k_pbmc() where a warning about non-unique variable names was being raised. Removed the unused _utils.py file. Removed the X_ranking entry from the obsm attribute of datasets. The _fit() function in nn_ranking.py now subsamples the data if max_samples is specified. The nn_ranking metrics now use subsampling in the _fit() function to improve performance. Fixed the git hash generation for openproblems functions Fixed a warning about pkg_resources being deprecated Removed unnecessary fetch-depth: 1 from workflow Fixed potential issue in _scanvi_scarches where labels_pred could be overwritten Fixed potential issue in _pred_xgb where num_round wasn't being used correctly Fixed an issue where baseline methods were not being filtered correctly from the benchmark results. Fixed an issue where metrics with all NaN values were not being removed from the benchmark results. Fixed an issue where some metrics were not being parsed correctly from the Nextflow output. Fixed an issue where the "mean_score" field was not being calculated correctly for each method. Fixed an issue where the "code_version" field was not being populated correctly for each method. Fixed an issue where the "submission_time" field was not being populated correctly for each method. Fixed an issue where the resource usage metrics were not being parsed correctly from the Nextflow output. Updated the run_tests workflow to skip testing on the test_website branch. Updated the run_tests workflow to skip testing on the test_process branch. Updated the create-pull-request step to set the author for the pull request. Updated the run_tests workflow to skip testing on pull request reviews. Updated the `update_website_ Full Changelog: https://github.com/openproblems-bio/openproblems/compare/v0.8.0...v1.0.0
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.340 | 0.407 |
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".