Advancing best practices for assessing trends of ocean acidification time series
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it