Total Column Ozone Variability Over Toronto, Ontario, Canada
Bibliographic record
Abstract
Ozone is capable of absorbing wavelengths (ultraviolet radiation) of biologically dam aging ultraviolet light. This radiation has been linked to health and environmental concerns. Most of this ozone (90 percent) is found in t he strat osphere, the layer of t he atmosphere lying between the altit udes of 10 and 50 kilometers (Kowalok 1993). Heat generated from this absorption causes the temperature to incre ase with altitude in the stratosphere. The resulting temperature profile is largely responsible for the dynamic stability of the stratosphere (Shen et al. 1995). Hence, the presence of the stratospheric ozone layer is vital both to human health an d to the dynamic stability of the stratosphere. Most research on ozone depleti on focuses on t he dramatic changes in t he Antarcti c Ozone Hole. The purpose of this paper is to ex amine the temporal variability in the thickness of the ozone layer over the Great Lakes area, as typified by data collected at Toronto, Ontario, Canada (Hosseinian 2000). We wish to address the following two questions: has there been a decrease in total column ozone in this region? And what is the source of interan nual and interdecadal variability in the total column ozone? In this study, statistical analysis is used to examine the trend in t he total column ozone concentration for the past four decades (1960 to 1998). Nonanthropogenic variations in the total ozone concentration are examined and some causal mechanisms for these variations are presented. The use of quantitative statistical analysis of the ozone data can readily enhance the search for unusual or ab normal changes in the ozone (Hill 1982). These analyses can be used to sep arate phys ical and chemical me chanisms from random variations.
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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 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".