Using Māori knowledge to assist understandings and management of shellfish populations in Ōhiwa harbour, Aotearoa New Zealand
Bibliographic record
Abstract
ABSTRACT This article discusses a marine research project which prioritised mātauranga Māori (Māori knowledge systems) to generate common management approaches and responses for the taonga (culturally important) species; Kūtai, Perna canaliculus , Green Lipped Mussel populations in Ōhiwa harbour, Aotearoa New Zealand. Findings from the trans‐disciplinary marine research project were used to develop a mussel management action plan (MMAP) which was endorsed and accepted in its entirety by the high‐level Māori tribal and Governmental partners of the Ōhiwa Harbour Implementation Forum (OHIF). This article provides an overview of research which used localised Māori knowledge systems to provide the foundations for improving, enhancing and safeguarding traditional mussel populations in the harbour. Further, this article critically positioned mātauranga Māori as an important and meaningful strategy for empowering Māori collaboration and voices in the wise use, care and practical management of marine taonga species for present and future generations.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".