Trends and variability of temperature and precipitation in Northwestern Ontario during the 20th century : implications for forest management
Bibliographic record
Abstract
Time series of temperature and rainfall in Northwestern Ontario are joined and \nadjusted to provide a database for analysis of regional climate change. \nProcesses used to locate and adjust for non-climatic discontinuities are provided. \nTemperature trends were computed for 1916-1998. All available stations have \nincreases in mean annual temperature. The greatest warming occurred in the \nspring at all stations with most stations > 1.0? C warmer. Some increase in \nwinter and the growing season temperature has taken place. A slight negative \nchange has taken place in the fall in most stations. The warming has been \ngreater In minimum temperatures than maximums with resulting declines in the \ndiurnal temperature range especially evident in the first half of the period. \nAverage rainfall has increased at all stations in the region. An analysis of daily \nrain events suggests increases in frequency and amounts of individual events \n>40 mm in southern locations and 30.0 - 39.9 mm In the northern stations. \nTrends and variability in these variables during the 20th century are analysed to \ndefine a base and context for predictions of significant climate change In the 21st \ncentury. Ecosystem change because of climate change is likely to have major \nimpacts on forest management and wood-based products, an economic sector of \nmajor importance in the region.
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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 | 0.002 |
| 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) | 0.002 | 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".