Modelling the relationship between catchment attributes and wetland water quality in Japan
Bibliographic record
Abstract
Abstract The influence of catchment attributes has been examined to find out whether variations in water quality indicators [electrical conductivity (EC), pH, turbidity, dissolved oxygen (DO), total dissolved solid (TDS), total nitrogen (TN), dissolved organic nitrogen (DON), dissolved inorganic nitrogen (DIN), temperature and nitrogen] could be explained by them for 24 wetlands in west Japan. Urban areas (%) were positively ( P ≤ 0.05) correlated with EC ( r = 0.67), TDS ( r = 0.69), TN ( r = 0.92), DON ( r = 0.60), [NH 4 + ] ( r = 0.47) and with [NO 2 − ] ( r = 0.50). Forest areas (%) were inversely ( P ≤ 0.05) correlated with EC ( r = −0.62), TDS ( r = −0.68), TN ( r = −0.68) and [NH 4 + ] ( r = −0.55) and with DON ( r = −0.43). Agricultural area (%) was positively ( P ≤ 0.05) correlated with EC ( r = 0.40), TDS ( r = 0.45), TN ( r = 0.44) and [NH 4 + ] ( r = 0.56) and with both areas (%) of grey lowland soil ( r = 0.60) and diluvial sand ( r = 0.58). Area (%) of regosol was positively correlated with DO (r = 0.42) but inversely with DON ( r = −0.44, P ≤ 0.05). Rhyolite was positively ( P ≤ 0.05) correlated with the TN ( r = 0.46) but inversely with DON ( r = −0.49) and [NH 4 + ] ( r = −0.47). Regression models were developed for the water quality indicators including EC ( r 2 = 0. 62), ( r 2 = 0.90), ( r = 0.52), TN ( r = 0.86), DIN ( r 2 = 0.74) and DON ( r 2 = 0.54) at 0.01 ≤ P ≤ 0.05. In the models, no significant contribution has been observed for catchment geometric features of the wetlands on water quality indicators. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".