Perspectives on in situ Sensors for Ocean Acidification Research
Bibliographic record
Abstract
As ocean acidification (OA) sensor technology develops and improves, in situ deployment of such sensors is becoming more widespread. However, the scientific value of these data depends on the development and application of best practices for calibration, validation, and quality assurance as well as on further development and optimization of the measurement technologies themselves. Here, we summarize the results of a two-day workshop on OA sensor best practices held in February 2018, in Victoria, British Columbia, Canada, drawing on the collective experience and perspectives of the participants. The workshop on In Situ Sensors for Ocean Acidification Research was organized around three basic questions: 1) What are the factors limiting the precision, accuracy and reliability of sensor data? 2) What can we do to facilitate the quality assurance/quality control (QA/QC) process and optimize the utility of these data? and 3) What sort of data or metadata are needed for these data to be most useful to future users? A synthesis of the discussion of these questions among workshop participants and conclusions drawn is presented in this paper.
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.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".