1 Kyoto Protocol: Effects on Agriculture By
Bibliographic record
Abstract
What if the atmosphere retained a greater amount of the sun’s energy due to a change its gaseous composition? What if instead of letting heat escape the earth’s atmosphere, more heat was reflected back to earth raising the tempera ure of the air, the sea, and the soil? The changes in levels of atmospheric gases have been predicted to cause a rise in ocean levels, the melting of polar ice caps, changes in precipitation patterns, and an increase in extreme weather occurrences. If negative climatic impacts are not mitigated through the stabilization of atmospheric conditions island nations, coastal communities, and developing countries will experience severe disturbances including flooding, famine, species migration and extinction, a d desertification, but these are not the only regions of the world that would suffer under the volatile weather conditions which could arise from climatic change (Bryce, 1999) (The Canada-Country Study). Canada could experience more extreme weather events, loss of soil moisture, immigration of n w pests, and increased soil erosion and degradation (Bryce, 1999, p.29) (Martin, 1991, p.27). As a country with semi-arid areas and areas prone to drought Canada is at risk to the negative impacts of climate change. Agricultural lands, hard hit by extreme weather events, increased soil moisture evaporation, and organic soil carbon loss, could be invaded by migrating pests and wildlife in search of new habitat and experience a 10-30 percent loss in yields (Environment Canada) (The Canada-Country Study). The concern about “global warming ” or the “Greenhouse Effect ” first arose on the international
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.149 | 0.150 |
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".