Utilising Indigenous seasonal knowledge to understand aquatic resource use and inform water resource management in northern Australia
Bibliographic record
Abstract
Summary Indigenous ecological knowledge can inform contemporary water management activities including water allocation planning. This paper draws on results obtained from a 3‐year study to reveal the connection between Indigenous socio‐economic values and river flows in the Daly River, Northern Territory. Qualitative phenological knowledge was analysed and compared to quantitative resource‐use data, obtained through a large household survey of Indigenous harvesting and fishing effort. A more complete picture of Indigenous resource‐use and management strategies was found to be provided by the adoption of mixed methods. The quantitative data revealed resource‐use patterns including when and where species are harvested. The qualitative Indigenous ecological data validated results from the quantitative surveys and provided insights into harvesting and resource management strategies not revealed by the discrete time‐bound surveys. As such, it informed the scientific understanding of patterns of resource use and relationships between people, subsistence use and river flows in the Daly River catchment. We recommend that natural resource managers, researchers and Indigenous experts prioritise collaborative projects that record Indigenous knowledge to improve water managers’ understanding of Indigenous customary aquatic resource use.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".