INVESTIGATING THE HISTORICAL CORRELATION BETWEEN ATMOSPHERIC CARBON DIOXIDE CONCENTRATION AND GLOBAL TEMPERATURE CHANGE
Bibliographic record
Abstract
Understanding the fundamental relationship between atmospheric carbon dioxide concentration and temperature rise is essential for tackling the problem of climate change that faces us today. Misconceptions regarding the relationship are widespread due to media and political influences. This investigation aims to address the popular misconception that CO2 concentration directly and naturally leads to global temperature rise. While anthropogenic CO2 emissions seem to affect the rising global atmospheric temperature with a confidence of 95%, it falters when the historical relationship using ice core data is studied. This investigation uses two statistical approaches to determine an accurate range and direction for this important relationship. Through a combined approach, it was found that historically CO2 concentration in the last 650 000 years lags global temperature rise by 1020-1080 years with a maximum correlation coefficient of 0.8371-0.8372. This result is important for the investigation of climate change.
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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) | 0.000 | 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".