Аномалии озонового слоя и погоды зимой 2013-2014 гг. В Северном полушарии: самый теплый декабрь в истории России; небывалые морозы в США; Олимпиада на дне озоновой дыры
Bibliographic record
Abstract
Monitoring of natural anomalies in real time and an explanation of the observed phenomena are an important scientific and social problem. In my subheading I compare world meteorological data information (from official and public Internet sources) with the one derived from the Canadian Brewer network (open access information from ozonesondes of ARQX & Canadian Upper Air Network, e-source Select Ozone Maps. Ozone and Ultraviolet Research and Monitoring. Environment Canada's World Wide Web Site. The Green LaneTM, Web. ). As a result I explain the origin of weather anomalies based on Earth Degassing Theory and Degassing Ozone Level Conception. From that point of view, the main cause of the weather (and climate) anomalies is the fluctuations of the general quantity of ozone in the atmosphere. These fluctuations are caused by the emission of the deep gases (hydrogen and methane), which destruct ozone, and the variations of the geomagnetic field, which increase the concentration of ozone. The positive ozone anomalies cool the troposphere and create anticyclones, which are dry, heavy and slow moving air masses. The negative anomalies warm up the air and create the cyclonic masses with law pressure. The closest anticyclones could move to that area bringing with them the anomalous temperatures, sometimes very high and sometimes very law. In my subheading, I have examined the ozone algorithm during the winter of 2013-2014 by comparing Roshydrometh monthly data and ozone anomalies average map for Northern hemisphere. From this perspective, I have analyzed unusually warm weather in Russia (including weather anomalies in Sochi during the 2014 Olympic Games), as well as extraordinary cold weather in the United States. Results of the comparison demonstrated the high correlation between zones and character of weather anomalies, on the one hand, and mapped areas of different-sign ozone anomalies, on the other hand.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.048 |
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; both teacher heads agree on what is shown here.
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".