Data processing for a small‐scale long‐term coastal ocean observing system near Mobile Bay, Alabama
Bibliographic record
Abstract
Abstract The oceanographic community routinely collects time series data of hydrography, water current velocity, and other basic physical, chemical, and biological properties of the marine environment. Such data are essential for establishing baseline characteristics of marine and estuarine ecosystems. However, the task of taking the raw data files as downloaded from a variety of instruments from multiple manufacturers, and converting them into file formats that can be used to address specific research questions, can be highly complex and time consuming. To illustrate some of these complexities, we have thoroughly documented the data processing steps for a small coastal ocean observing system near Mobile Bay, Alabama, that has been in operation since 2004. Our goals were to produce documentation and data provenance in sufficient detail for full science reproducibility of all studies that use data from this system, provide a template for other ocean observation operations, and highlight a need for better recognition of the significant amount of time and expertise often required to do both the data processing and the documentation for long‐term observational systems.
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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".