<scp>QFASA</scp>: A Comprehensive R Package for Diet Estimation via Fatty Acid Signature Analysis
Bibliographic record
Abstract
Quantitative fatty acid signature analysis (QFASA) is a well-established diet estimation method that has been used extensively on a wide variety of marine mammal species. The method, along with its new refinements and extensions, requires the use of statistically intricate tools, many of which are computationally demanding. Recent developments in QFASA include a maximum likelihood framework for diet estimation, statistically valid inference procedures such as confidence intervals for the diet and hypothesis tests for comparing fatty acid signatures and/or diets, a measure of repeatability in the diet estimates, a prey species selection algorithm, as well as novel ways to estimate calibration coefficients, which are used to improve accuracy in the estimates. The QFASA R package was developed to facilitate access to the latest statistical QFASA tools and provide a means of efficiently disseminating new QFASA-related research, often developed by statisticians in collaboration with biologists. Further, using up-to-date functions ensures that QFASA methods are being applied in a legitimate and consistent manner. In this work, we present the QFASA R package, highlighting key functions for diet estimation and demonstrating their use with sample data available in the package. The QFASA R package is user-friendly, offers a broad range of functionality, and the vast majority of the functions are unique to this package.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.114 | 0.090 |
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".