Towards a better understanding of First Nations perspectives of monitoring, management, and values of Great Barrier Reef Sea Country
Bibliographic record
Abstract
Focussing on the Cairns Area Plan of Management (CAPOM) and the Keppels Capricorn Bunkers (KCB) spatial areas, the SEABORNE project established and tested a proof of concept to organise existing data and quantify benefits derived from GBR ecosystem services by end users. Hereafter, this is referred to as an Ecosystem Service Value Chain (ESVC). End users included households, Reef-dependent businesses, Traditional Owners, and Governments. Through an ESVC lens, the project team looked at existing data sets to determine which data linked together to give a full account of value of an ecosystem service to an end user and which data provides additional information. This is a form of benefit transfer. The ESVC approach is a linear approach. Working with First Nations people it became clear that this approach was not appropriate to understand the richness of interaction of First Nations people with the Sea Country of the Reef and the values generated from this. Therefore a different, more culturally appropriate approach was taken.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".