ropensci/targets: New helpers, better relaying in Target Markdown, and better exception handling
Bibliographic record
Abstract
targets 0.7.0 Bug fixes Ensure error = "continue" does not cause errored targets to have NULL values. Relay output and messages in Target Markdown interactive mode (using the R/default knitr engine). New features Expose the poll_connection, stdout, and stderr arguments of callr::r_bg() in tar_watch() (@mpadge). Add new helper functions to list targets in each progress category: tar_started(), tar_skipped(), tar_built(), tar_canceled(), and tar_errored(). Add new helper functions tar_interactive(), tar_noninteractive(), and tar_toggle() to differentially suppress code in non-interactive and interactive mode in Target Markdown (#607, @33Vito). Enhancements Handle future errors within targets (#570, @stuvet). Handle storage errors within targets (#571, @stuvet). In Target Markdown in non-interactive mode, suppress messages if the message knitr chunk option is FALSE (#574, @jmbuhr). In Target Markdown, if tar_interactive is not set, choose interactive vs non-interactive mode based on isTRUE(getOption("knitr.in.progress")) instead of interactive(). Convert errors loading dependencies into errors running targets (@stuvet).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.113 |
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".