An eco asset ontology towards effective eco asset management
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
Purpose The purpose of this paper is to develop an ontology of eco or natural assets to represent eco asset knowledge at two levels: eco asset metal model and eco asset ontology (EA_Onto). The three objectives of this paper are to: define eco assets explicitly to reach a common understanding of the terms; evaluate the ontology; and discuss a potential area of application. Design/methodology/approach A seven-step methodology was used to develop the proposed ontology: define the scope; develop the eco asset meta model (EA_MM), define taxonomy, code ontology, capture ontology, evaluate ontology and document ontology. Findings The EA_MM was developed to represent eco asset domain knowledge, which was further extended to develop the EA_Onto, explicitly defining the eco asset knowledge in asset management. As a part of evaluation, it was found that the knowledge representation is consistent, concise, clear, complete and correct. Practical implications Theoretically, the proposed ontology is a significant contribution to the body of knowledge in asset management. Practically, the knowledge representation provides a common understanding of eco assets for asset management experts. In addition, it will be used in applications for effective eco asset management. Originality/value The current literature lacks explicit declaration of eco assets, how they are related to built environment for effective integration and how asset management functions are to be applied to accomplish effective eco asset management. Presently, eco assets are managed on an ad hoc basis, which need to be explicitly defined through developing an EA_Onto for implementation in applications for effective eco asset management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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