Bibliographic record
Abstract
This article firstly proposes a fuzzy weighted moving average (FWMA) to compute well for the beginning and end parts. Then, the FWMA is applied to both of the annual anomalies and the annual standardized differences (variance adjusted anomalies) of temperature among the six top nations or regions of cumulative CO2 emissions, Canada and the globe to reveal and compare the interdecadal trends. The related topic of ‘land/sea warming contrast’ was re-analyzed with the FWMA. The main findings are somewhat unexpected as follows: (1) The FWMA curves of anomalies showed that all six nations got warming up stronger than the globe except for India. But the sequence order of the nation’s warming extents in the last decade were much different from those of nation’s cumulative CO2 emissions. (2) The FWMA of the annual standardized differences of temperature showed much better than the annual anomalies. The sequence order of nation’s warming up agreed with that of the cumulative CO2 emissions among the seven nations. In contrary, the globe got warming obviously higher than all the seven nations or regions. The direct reason, in statistics, is that the climatologic variance of the globe is much smaller than all the seven nations, in statistics. (3) The related phenomena of ‘land/sea warming contrast’ appeared only in their anomalies, but disappeared all in the standardized differences. The direct reason is depended upon the around 2 times of difference of the climatologic variance between the land and sea. (4) The FWMA curves for the globe actually much closes to that of the sea than the land.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".