Bibliographic record
Abstract
The Gibsons Experience - Nature is our most valuable infrastructure asset. Session Description: Gibsons’ natural capital assets, and the ecosystem services they provide, are a fundamental and integral part of the Town’s infrastructure. Natural capital assets provide clear advantages over engineered (or grey) infrastructure. They are cheaper to operate and maintain, if not degraded; may provide “free” ecosystem services; do not depreciate, if properly managed, and are carbon neutral, or even carbon-positive. Gibsons is one the first Canadian municipalities to explore managing the natural capital in our community, such as green space, aquifers, foreshore area and creeks, using infrastructure and financial management concepts that are systematically applied to managing engineered assets. Our rationale is that the natural services provided by these systems, in the form of rainwater management, flood control and water purification, have tangible value to the community as, or more, effective as engineered infrastructure. Bringing these natural assets into the same asset management system as engineered infrastructure recognizes the quantifiable value they provide to the community and integrates them into the municipal framework for operating budgets, maintenance and regular support. Many of us are unaware of the infrastructure role played by parts of our natural environment and so we may not take the kinds of precautions that preserve our natural municipal infrastructure in good working condition. Gibsons is blessed with many natural assets. The following examples provide direct municipal services: The Gibsons aquifer (water storage and filtration), creeks, ditches, wetlands (rain water management) and the foreshore area (natural seawall).
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".