Bibliographic record
Abstract
Acid rain continues to be a major environmental problem. Canada has been monitoring indicators of acid rain in various ecosystems since the 1970s. This project focuses on the analysis of a selected subset of data generated by the Turkey Lakes Watershed (TLW) monitoring program from 1980 to 1997. TLW consists of a series of connected lakes where 6 monitoring stations are strategically located to measure the input from an upper stream lake into a down stream lake. Segment regression models with AR(1) errors and unknown point of change are used to summarize the data. Relative likelihood based methods are applied to estimate the point of change. For pH, all the regression parameters except autocorrelation have been found to change significantly between the model segments. This was not the case for SO4 2- where a single model was found to be adequate. In addition pH has been found to have a moderate increasing trend and pronounced seasonality while SO4 2- showed a dramatic decreasing trend but little seasonality. Multivariate dimension reduction methods are used to provide an overall graphical summary of the changes in TLW water system. We also report the result of applying segment regression for the analysis of first two principal components in selected stations. The results show that the efforts of the Canadian and US governments to reduce the emission of SO2 have been successful in controlling the acid rain problem in Eastern Canada. The project ends with suggestions for various extensions of the present work.
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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 | 0.000 | 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) | 0.044 | 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".