The Extending Ocean Drilling Pursuits (eODP) Project: Synthesizing Scientific Ocean Drilling Data
Bibliographic record
Abstract
Abstract For over 50 years, cores recovered from ocean basins have generated fossil, lithologic, and chemical archives that have revolutionized fields within the earth sciences. Although scientific ocean drilling (SOD) data are openly available following each expedition, the formats for these data are heterogeneous. Furthermore, lithological, chronological, and paleobiological data are typically separated into different repositories, limiting researchers' abilities to discover and analyze integrated SOD data sets. Emphasis within Earth Sciences on Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles and the establishment of community‐led databases provide a pathway to unite SOD data and further harness the scientific potential of the investments made in offshore drilling. Here, we describe a workflow for compiling, cleaning, and standardizing key SOD records, and importing them into the Paleobiology Database and Macrostrat, systems with versatile, open data distribution mechanisms. These efforts are being carried out by the extending Ocean Drilling Pursuits (eODP) project. eODP has processed all of the lithological, chronological, and paleobiological data from one SOD repository, along with numerous other data sets that were never deposited in a database; these were manually transcribed from original reports. This compiled data set contains over 79,899 lithological units from 1,125 drilling holes from 422 sites. Over 26,000 fossil‐bearing samples, with 5,378 taxonomic entries from 13 biological groups, are placed within this lithologic spatiotemporal framework. All information is available via GitHub and Macrostrat's application programming interface, which renders data retrievable by a variety of parameters, including age, site, and lithology.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".