Contaminants in water, sediment and fish biomonitor species from natural and artificial estuarine habitats along the urbanized Gold Coast, Queensland
Bibliographic record
Abstract
Metal and pesticide contaminants were measured in water, sediment and fish species in various Gold Coast waterways, Queensland. With the exception of Cu, metal concentrations in water, measured using the diffuse gradients in a thin film (DGT) technique, complied with relevant Australian guidelines. Cu concentrations in these waterways have been related to recreational vessel activities previously. All sediment metal concentrations measured were below the national guidelines, although Cu, Zn and Pb were found to vary significantly between habitat types. Evidence of spikes in sediment pesticide concentrations (some banned over 50 years ago) was observed in some artificial residential waterways. Heavy metals and pesticides were measured in the tissue (muscle, gills and liver) of three economically important species of fish, with different feeding strategies (partly herbivore Arrhamphus sclerolepis, carnivore Acanthopagrus australis, detritivore Mugil cephalus). We tested the hypothesis that fish accumulate different amounts of contaminants from wetland habitats affected by different intensities of anthropogenic activities (i.e., marinas, artificial residential canals, artificial residential lakes, estuaries and natural, vegetated waterways). Significantly higher concentrations of Cu were found in the gills of each fish species from marinas compared to fish caught in other waterways. Furthermore, fish caught in canals had the second highest Cu and natural waterways the lowest. These results support the stated hypothesis for Cu and furthermore indicate that these fish species are suitable as biomonitors in estuarine waterways. Metal and pesticide concentrations in the edible muscle tissue of all fish complied with the Australian Food Standard Code recommended limits for human consumption, apart from As which is likely to be due to bioconcentration of lower toxicity organo-As species. These results indicate a low health risk for humans consuming fish, in terms of contaminant levels. The accumulated body of evidence on contaminants within Gold Coast waterways generally suggests that there are no major threats of metal or pesticide contamination, except for marina facilities which are a major source of Cu which also accumulates in fish. Water quality threats are also highlighted in residential canals, presumably as a consequence of their hydrological design.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".