A ‘Mosaic’ Perspective of Climate Due to Natural and Societal Influences
Bibliographic record
Abstract
In response to the recent statement by the UN General Assembly President that only 11 years remain to avert global climate catastrophe, this paper investigates the effects of human population on climate and the ability to sustain ourselves, and finds that claims of crisis are overstated. UN data show that world agricultural food crops substantially exceed population growth, Canada reports that forest fires decreased markedly in number since 1990, other federal agencies show that atmospheric CO2 mimics population growth and is therefore likely anthropogenic, they report that natural isostacy and groundwater-taking explains some coastal land subsidence, and that much flooding is due to impervious surfaces in urban areas. Government data for rural and urban Ontario Canada show that there is insufficient correlation between air temperature and CO2 concentration to conclude that CO2 is creating heat as a greenhouse gas. Instead, urban ‘climate islands’ that produce heat and CO2 concomitantly appear as the driver of apparent climate change. One representation is that of large cities being stationary source nodes of permanent heat and CO2 broadcasting outwards into their surrounds. This perspective is akin to a series of local urban-rural ‘mosaics’ instead of a global melting pot, and as such it is also potentially uncovering the central roots of climate change and not simply the averaged symptoms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".