Towards a unified ontology for monitoring ecosystem services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it