Phytoplankton responses to nutrient sources in coastal waters off southeastern Australia
Bibliographic record
Abstract
Reports of visible algal blooms have increased in New South Wales (NSW) coastal waters since 1990. Our three-year, multi-disciplinary study assessed the relative importance of natural and anthropogenic nutrients on the development of phytoplankton blooms in the waters between Port Stephens and Jervis Bay. The hinterland of this region accommodates 85% of the population of the 6.5 million inhabitants of New South Wales, Australia. Three deepwater outfalls represented the principal, continuous, anthropogenic nutrient source with nitrogen mainly in the bioavailable form of ammonia. Sewage effluent typically remained submerged especially during the spring-summer period when algal blooms occur most frequently. On average, coastal catchments contributed relatively small loads of nutrients except during major flood events because extensive estuaries tend to buffer nutrient fluxes to the ocean. Episodic slope water intrusions were the principal source of nitrogen (nitrate) to coastal waters especially during spring and summer. Phytoplankton blooms appeared to occur in response to slope water intrusions irrespective of proximity to other major nutrient sources. A new understanding of mechanisms of slope water intrusion emerged from model simulations and direct observations. A major upwelling event in January 1998, towards the end of the 1997/98 El Niño period, demonstrated the importance of large scale slope water intrusions on the development of algal blooms. Although natural upwelling/uplifting was found to be the principal driver for major algal blooms, it is possible that more subtle impacts of anthropogenic nutrients may be masked by ‘natural’ variability including that due to the El Niño Southern Oscillation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".