Significant Errors Identified in the IPCC Reports
Bibliographic record
Abstract
Some countries are concerned about increasing levels of carbon dioxide (CO2) causing dangerous warming of the Earth, as promoted by the Intergovernmental Panel on Climate Change (IPCC). This is a mistake by the IPCC because the warming by CO2 is too small to measure. This study identifies six serious errors by applying critical thinking and the scientific method. For example, doubling the level of CO2, i.e., the Equilibrium Climate Sensitivity (ECS), does not measurably affect the Earth’s temperature because the warming effect of carbon dioxide (CO2) is too small to measure. Thus, the plot of the warming effect of CO2 against the level, whether or not it is a straight line or logarithmic, is irrelevant. The Sun’s energy is the primary control of Earth’s temperature. However, there is some energy input from the ENSO (El Niño Southern Oscillation). Thus, the Earth's temperature drops when the Sun’s energy output falls, as it is currently doing. The IPCC overestimates the warming impact of methane and nitrous oxide, both of which have a negligible warming effect. The Earth’s energy balance is incorrect because it is based on averages, and the amount of energy to evaporate water must equal the amount sent to space. It is recommended that people and their governments know about these errors and take appropriate action.
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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 | 0.035 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".