Advancing best practices for assessing trends of ocean acidification time series
Bibliographic record
Abstract
Assessing the status of ocean acidification across ocean and coastal waters requires standardized procedures at all levels of data collection, dissemination, and analysis. Standardized procedures for assuring quality and accessibility of ocean carbonate chemistry data are largely established, but a common set of best practices for ocean acidification trend analysis is needed to enable global time series comparisons, establish accurate records of change, and communicate the current status of ocean acidification within and outside the scientific community. Here we expand upon several published trend analysis techniques and package them into a set of best practices for assessing trends of ocean acidification time series. These best practices are best suited for time series capable of characterizing seasonal variability, typically those with sub-seasonal (ideally monthly or more frequent) data collection. Given ocean carbonate chemistry time series tend to be sparse and discontinuous, additional research is necessary to further advance these best practices to better address uncharacterized variability that can result from data discontinuities. This package of best practices and the associated open-source software for computing and reporting trends is aimed at helping expand the community of practice in ocean acidification trend analysis. A broad community of practice testing these and new techniques across different data sets will result in improvements and expansion of these best practices in the future.
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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 | 0.197 | 0.541 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".