Relationships between land use and nutrient concentrations in streams draining a ‘wet-tropics’ catchment in northern Australia
Bibliographic record
Abstract
Differences in stream nutrient concentrations typically reflect upstream differences in land use. In particular, nitrate concentrations are greatly increased by losses from nitrogen (N) fertiliser applied to areas of intensive cropping. In the present study, a relationship between the area of such land use and the nitrate concentrations in the receiving streams was predicted. This relationship was tested using several data sets from the Tully basin, in the wet-tropics bioregion of north Queensland, Australia. The proportions of fertiliser-additive land use (FALU), mostly sugarcane and bananas, were correlated with the concentrations of nutrients in streams that drain these land uses. The data compared included two long-term sampling studies in the Tully River catchment and more recent, broader catchment sampling and plot-scale studies in this region. A strong relationship was shown for nitrate, but weaker relationships were observed for other N-nutrient and P-nutrient forms. Comparisons were made with contemporary and historical land-use changes in the Tully basin. The strong relationship of FALU with nitrate provides evidence that the nitrate exports from this catchment are largely derived from fertiliser use. This relationship can be used to derive nitrate run-off coefficients for fertilised land use in catchment models or to monitor changes following management to reduce fertiliser usage.
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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".