Dispersal of suspended sediments and nutrients in the Great Barrier Reef lagoon during river-discharge events: conclusions from satellite remote sensing and concurrent flood-plume sampling
Bibliographic record
Abstract
Intense wet-season rainfall in January 2005 caused rivers in the Mackay–Whitsunday region of Queensland, Australia, to produce large discharges to the Great Barrier Reef (GBR) lagoon. The regional land use is dominated by sugarcane cultivation, beef grazing and urban uses. The high nutrient (nitrogen and phosphorus) fluxes from these land uses via river runoff produced a massive phytoplankton bloom in the GBR lagoon, which, after 9 days, had spread 150 km offshore. The plume and algal bloom surrounded inner-shelf reefs of the GBR such as Brampton Island Reef and its spread was tracked with a variety of satellite sensors including MODIS, SeaWiFS and Landsat over the 9-day period. The ability to be able to access imagery from a large number of satellite sensors allowed almost daily estimates of the extent of plume to be made, despite periods of cloud. Analysis of water samples from the plume revealed elevated (2–50 times higher) concentrations of Chlorophyll a (and hence phytoplankton biomass), up to 50 times higher than in non-flood conditions, nutrients (2–100 times higher) and herbicide residues (10–100 times higher) compared with GBR lagoon waters in non-discharge conditions. The concentration data from the samples and estimated exposure periods from the satellite images allowed estimates of the exposure of GBR marine ecosystems (coral reefs, the pelagic community, seagrass beds and mangrove forests) to the terrestrial contaminants to be made.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".