pfmc-assessments/nwfscSurvey: Add new composition functions and option to turn off standard filtering
Bibliographic record
Abstract
The following functions have been added to the package: get_expanded_comps(): This function estimates expanded composition data for length or age data. This function expands the composition data in the same manner as the SurveyLFs.fn() and the SurveyAFs.fn() functions. The SurveyLFs.fn() and the SurveyAFs.fn() functions remain in the package but will deprecated at some point in the future once users are comfortable with the new get_expanded_comps() function. get_input_n(): This function calculates input sample size via three alternative methods: total samples, number of positive tows, or based on Stewart and Hamel, 2014. This function is similar to the existing GetN.fn() function but has expanded functionality. Additionally, the new get_input_n() function is called within the get_expanded_comps() functions for SS3 formatted composition data. The following functions have been modified: All of the pull_* functions have been modified to allow users to either apply standard data filtering or to retain all data via standard_filtering argument. Additionally, the data pulling functions now report information on the number of samples that are removed with standard_filtering = TRUE if verbose = TRUE.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.351 | 0.270 |
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".