Construction of marine vocabularies in the Marine Metadata Interoperability Project
Bibliographic record
Abstract
Data producers often overlook existing metadata descriptors and controlled vocabularies for describing their data, opting instead to create custom descriptors and vocabularies. One of the objectives of the Marine Metadata Interoperability (MMI) project is to reduce this vocabulary proliferation. The work is accomplished as community collaborations, which are supported via the content management framework of the MMI Web site. Services, processes, techniques, and advice are all offered via the community Web site supported by the MMI (http://marinemetadata.org). As part of the services, MMI has created and made available marine ontologies based on existing vocabularies. Ontologies are an explicit and formal specification of mental abstractions. Ontologies are being published using the Web Ontology Language (OWL), the ontology expression tool recommended by the World Wide Web Consortium (W3C), and made available using Web services. By providing these services using common terminology, the MMI effort facilitates discovery, sharing, and markup of marine data. The MMI methodology for creating the marine ontologies is composed of: identification, harmonization, alignment and mapping, and publication. First the marine vocabulary is identified. The namespace and the required transformation are then documented. Then the vocabulary is harmonized with the other vocabularies by transforming the vocabulary into a common structure, in this case OWL format. Having all the marine vocabularies harmonized in OWL allows alignment and mapping between the vocabularies. OWL allows the required mapping relationships such as "same As", "narrower Than", and "broader Than". Finally, the ontologies are published via Web services. The first three parts of this methodology, which form the foundation for the development of true semantic interoperability, will be discussed in this paper.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".