Bibliographic record
Abstract
Recently The Economist (2013a), a prominent journalistic advocate of strong policies to control C[O.sub.2] emissions, expressed their puzzlement on the absence of warming over the last 15 years. They observed that this flat period of global average temperature occurred despite that C[O.sub.2] emissions from human sources continued at an increased rate. The total human-produced C[O.sub.2] emissions in that period of flat temperatures represent a quarter of all such emissions ever produced. The standard climate models, such as those used by the United Nation's International Panel on Climate Change (UN IPCC), anticipated that such massive C[O.sub.2] increases should have caused continuing increases in average global temperatures. The Economist noted that observed global average temperature is now at the lowest end of the predicted range, and that if the present trend continues, the actual temperatures will soon be below even the lowest forecasts. Most recently, Fyfe, Gillett, and Zwiers (2013) demonstrated that the current climate models have experienced a systematic failure--a finding very similar to Knappenberger and Michaels (2013). Given the large difference of observed data from the forecasts that underlie much current policy, it is timely to ask if the climate debates are addressing the right questions. Comparison of forecasts to observations is the right way to start asking. If the forecasts used to set policy are not accurate, then policies based on those forecasts warrant review. This is important for all of the purposes for which climate policies may be set, but this article concentrates on country development policies related to energy, especially electricity. Those policies are critical, because it is widely accepted that more than one billion people have no access to reliable electric grid power and therefore must turn to other sources for heat and light (Ballonoff 2013). The cost to provide that electricity, and also meet the continued and expanding needs of developed and developing countries, is estimated in the trillions of dollars. Our understanding of climate change and how it interacts with continued expansion of use of energy resources thus has a profound effect on assuring such huge capital cost is invested in the most effective way. The Status of Climate Science The foundation of the modern climate change discussion is the accurate observation that human activity has significantly increased the atmospheric concentration of C[O.sub.2], and that such activity is continuing (Tans 2009). Increased C[O.sub.2] concentration, especially when amplified by predicted feedback effects thus also is assumed to predict increasing global average atmospheric temperature. Depending on the degree of warming expected, other serious and mainly undesired effects are predicted. As The Economist (2013a) observed, the average global temperature did rise on average over the previous century. Following a 25-year cooling trend post-World War II, temperatures increased at an especially strong rate in the quarter century ending in 1997. The trend of that warming period, the correlation with increased C[O.sub.2], and the fact of human activity causing that C[O.sub.2] increase apparently supported use of projection models extending that trend to future years. Such projections were the basis for the UN's 1997 IPCC analysis on which much current policy is based. It is thus at least ironic that 1997 was also the last year in which such measured global average temperature increase took place. One of the key features of the IPCC forecast, and greenhouse effect forecasts generally, is the expected feedback loops. One of those is that the presumed drier and hotter conditions on the ground would cause expanded desertification and deforestation. A distinct kind of greenhouse effect is also predicted from increased C[O.sub.2] concentration--namely, the aerial fertilization effect, which is that plants grow better in an atmosphere of higher C[O. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".