Bibliographic record
Abstract
Carbon valuation of aquatic ecosystems is an important tool that can both mitigate for and adapt to climate change. Estuary ecosystems play an important role in climate change as well as watershed and community health, and are located where coastal communities thrive. Of the many important functions of estuaries are the cycling and sequestration of carbon in sediments. A staggering 55% of all living carbon is cycled in the ocean and 50- 70% of that is stored via estuary vegetation and sediments. Despite this, estuary habitats have only recently been explored as an option for carbon offsets in British Columbia. Crucial to the development of a carbon offset system that uses eelgrass and salt marsh conservation and restoration is the Comox Valley Project Watershed Society (“Project Watershed”). Project Watershed is a non-profit group on Vancouver Island that is applying a scientific approach combined with volunteer resources to collect carbon data in the eelgrass and saltmarsh habitats of the K’omoks Estuary. This data will contribute to carbon offset models that will form the framework for a province-wide Blue Carbon Protocol. To accomplish this, Project Watershed has gained the support of the Province of BC and Vancouver Island University through a Memorandum of Understanding. Together this partnership will work towards the restoration and conservation of these important ecosystems and the values they provide, while ensuring communities benefit through education opportunities and climate change adaptation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".