Bibliographic record
Abstract
Overview This is a minor release that adds a few new functionalities, in particular a method to combine multiple EchoData objects, addresses a few bugs, improves packaging by removing pinning for dependencies, and improving the testing framework. New features Add a new method to combine multiple EchoData objects (#383, #414, #422, #425 ) Potential time reversal problems in time coordinates (e.g., ping_time, location_time) are checked and corrected as part of the combine function The original timestamps are stored in the Provenance group Add a new method compute_range for EchoData object (#400) Add subpackage metrics and supply functions to compute summary statistics (#402) Allow flexible extensions for AZFP files in the form ".XXY" where XX is a number and Y is a letter (#428) Bug fixes Fix the bug/logic problems that prevented calibrating data in EK80 files that contains coexisting BB and CW data (#400) Fix the bug that prevented using the latest version of fsspec (#401) Fix the bug that placed echosounder_raw_transmit_samples_i/q as the first ping in echosounder_raw_samples_i/q as they should be separate variables (#427) Improvements Consolidate functions that handle local/remote paths and checking file existence (#401) Unpin all dependencies (#401) Improve test coverage accuracy (#411) Improve testing structure to match with subpackage structure (#401, #416, #429 ) Documentaion Expand Contributing to echopype page, including development workflow and testing strategy (#417, #420, #423)
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.167 | 0.200 |
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".