The gulf R package: quality assurance and quality control of presence, abundance and biomass indices derived from the annual September ecosystem survey of the southern Gulf of St. Lawrence (1971-2021)
Bibliographic record
Abstract
Time-series of presence, catch abundance and catch biomass were computed for 90 taxa using data from the annual September ecosystem survey conducted in the southern Gulf of St. Lawrence. Time-series were first computed using a legacy script written in SAS, and compared to corresponding time-series computed in the R programming language using a bespoke package called gulf. Time-series computed by the two software platforms were compared by plotting them and computing correlation coefficents. After performing a number of database corrections and developing appropriate functions, the gulf package successfully reproduced the results obtained by the SAS software, achieving an intra-class correlation coefficent value exceeding 0.999 for all time-series of presence, abundance and biomass. The functions implemented in the gulf package are briefly described and the results of time-series comparisons are presented through figures and tables reporting the different correlation coefficients. Minor discrepancies between the time-series resulted from cases when weights derived from length measurements differed from recorded catch weights.
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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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".