Towards a unified ontology for monitoring ecosystem services
Bibliographic record
Abstract
• Operationalising the language of ecosystem services remains a barrier to progress. • A formal ontology that organises terms and data is needed to support operationalisation. • We propose a formal ontology for monitoring ecosystem services. • Conceptual clarity enables data integration and automation. • Collective efforts are required for the field to develop this tool further. Ecosystem services (ES) are an important part of global and national environmental policies. In this context, there is a call for the monitoring of ES to support their management. However, the proliferation of terms used within ES science is a barrier to standardised monitoring. Monitoring ES requires knowing exactly what variables to measure and how they relate to change in the states of ES. It further requires interoperability between methodologies used by information systems to operationalise data flows. Here, we aim to systematise the language used to define ES and the terminology used in their monitoring by developing an ontology for ES monitoring. Ontologies are tools that operationalise concepts and the relationships among terms used to define them. An ontology allows people and machines to use terms consistently. Building on advances in other disciplines, the ES monitoring ontology systematises the language of ES across major conceptual frameworks advancing conceptual clarity and operationalisation of ES. We test the ES monitoring ontology with data from three ES in British Columbia, Canada, to highlight how it can enable information sharing and monitoring. We show that the ontology can organise and retrieve information and data for ES monitoring in a systematic way. Our work contributes to advancing interoperability of ES, taking a step towards systematically understanding ES change with automated tools. We invite members of the ES community to join the effort of developing this ontology for ES so that can it contribute to the challenge of systematically monitoring change in social-ecological systems.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| 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".