MULTIVARIATE ANALYSIS OF WATER QUALITY IN THE RICHIBUCTO DRAINAGE BASIN (NEW BRUNSWICK, CANADA)<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT: Specific conductivity, pH, dissolved oxygen, carbon, phosphorous, and nitrogen species were measured at 36 stations in the Richibucto River drainage basin, including the estuary, in New Brunswick, Canada, over the six‐year period 1996 through 2001. Each station was sampled between 1 and 26 times (mean = 7.5, standard deviation = 6.0) during the ice free seasons without regard to tide. There was significant variance among stations in most parameters. Principal component analysis (PCA) was used to identify the processes explaining the observed variance in water quality. Because of the high variability in specific conductance, stations were first grouped in a freshwater subset and an estuarine (brackish water) subset. For freshwater stations, most of the variance in water quality was explained by pH and total organic carbon, as well as high nutrient concentrations. These high nutrient concentrations, along with water salinity, which varies with flow and tides, are also important in determining water quality variability in brackish water. It is recommended that water quality parameters that were found to explain most of the variance by PCA be monitored more closely, as they are key elements in understanding the variability in water quality in the Richibucto drainage basin. Cluster analyses showed that high phosphorous and nitrate concentrations were mostly found in areas of peat runoff, tributaries receiving treated municipal effluent, and lentic zones upstream of culverts. Peat runoff was also shown to be acidic, whether it is runoff from a harvested area or a natural bog.
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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 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".