The Evaluation of Temporal and Spatial Trends of Global Warming and Extreme Ocean Surface Temperatures: A Case Study of Canada
Bibliographic record
Abstract
In recent years, extreme weather has frequently occurred worldwide and caused significant disasters, including large-scale forest fires, rare heat waves, heavy rains, floods, and tornadoes. Those have caused unprecedented losses of human lives and property in some countries, affecting the livelihoods of many people. Climate change and natural disasters are the two hotspots of scientific research today, and there is a certain degree of correlation between the two. Although countries worldwide have long known about climate change and its threats to human existence and have been discussing countermeasures, they have still not been able to carry out concerted and practical actions. The study takes Canada as an example, and selects six representative provinces to evaluate the temporal change characteristics of extreme temperature at different sites. We use MATLAB software to perform multiple linear regression, linear fitting methods, and Pearson correlation analysis to analyze spatial changes and time-space trends. The method studies the relationship between the emergence of extreme weather and climate change and uses the evolutionary game theory to explore whether there is any contradiction between global warming and extreme local cold. The study found: (i) The maximum temperature of most provinces in Canada will be constantly higher, and the minimum temperature will be lower. Generally speaking, the average temperature of each year is slowly decreasing. (ii) The average temperature data of British Columbia (Eastern Pacific) and Quebec (West Atlantic) show that ocean temperature has a specific effect on land temperature in surrounding areas. (iii) Pearson correlation analysis shows that the emergence of extreme weather is closely related to climate change. (iv) The evolution path of the two-party game shows that global warming and the occurrence of extreme local cold are not contradictory. Under the conditions, there is a certain degree of synchronization between the two, interacting and influencing each other.
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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 | 0.001 | 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 | 0.001 |
| 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".