Merged Observatory Data Files (MODFs): An Integrated Observational Data Product Supporting Process-Oriented Investigations and Diagnostics
Bibliographic record
Abstract
Abstract. A large and ever-growing body of geophysical information is measured on campaigns and at specialized observatories as a part of scientific expeditions/experiments. These collections of observed data include many essential climate variables (as defined by the World Meteorological Organization), but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment, but also the drivers that contribute to that state. These field data are not only used to further understand the environmental processes through observation-based studies, but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation-model research, the Merged Observatory Data File (MODF) concept has been developed. The MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel development of corresponding Merged Model Data Files (MMDFs). Using MODF and MMDF protocols will facilitate the evolution of Model Intercomparison Projects into Model Intercomparison and Improvement Projects by putting observation and model data ‘on the same page’ in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during this first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can thus be considered a MODF creation manual.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".