Identifying the land-based sources of suspended sediments, nutrients and pesticides discharged to the Great Barrier Reef from the Tully–Murray Basin, Queensland, Australia
Bibliographic record
Abstract
To assist in the development of the Tully Water Quality Improvement Plan, a subcatchment water quality monitoring program was undertaken to identify the pollutants of concern and their land-based sources. Monitoring of suspended sediments, nutrients and pesticides in subcatchment waterways was conducted during the 2005–06 and 2006–07 wet seasons, which both had above average annual flows. We found distinct water quality signals from the basin’s major land uses (forest, grazing, urban, sugarcane and banana cultivation), except for suspended sediment concentrations, which were low across all land uses when compared with neighbouring river catchments. This reflects the high ground cover of the basin and the location of intensive agriculture on low sloping areas of the floodplain, minimising the potential for erosion. Nitrate concentrations were elevated in streams draining sugarcane, indicating fertiliser export from intensive agricultural landscapes. Residues of the herbicides diuron and atrazine were detected at sites draining sugarcane, and on occasion exceeded national ecological protection trigger values, which highlights a potential threat to downstream wetlands of recognised national significance. Herbicides were also detectable offshore in flood plumes of the Tully–Murray Rivers, with some concentrations of diuron above lowest observable effect concentrations for specific species of seagrass and corals. Run-off of nitrate and diuron were identified as key water quality issues in the Tully–Murray basin.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".