Bibliographic record
Abstract
The world is right now facing twin issues on the environment - one is the ongoing El Nino having implication for South Asia monsoon and the other is the warmest average earth temperature of 17.18 degree C on July 3/4 earlier this month. It is no coincidence this warmest day is also the record of global warming in record keeping years that means a close to two centuries.      First, El Nino: though monsoon in South Asia is active its Yo-Yo behaviour is a cause of worry with droughts and floods alternating in different parts including areas of China. We can have only hope that the remaining two to three months of monsoon will be normal in terms of spread and intensity so that agriculture - back bone of food security in the most populous nation in the world is moderated.      Second, mean global temperature: it is already over the limit of what various nations agreed in terms of limiting their CO2 emission for next few decades leaving behind asking questions such as what else we can do now? It is better to be not too late instead of being “better be late than never” approach. Forest bush fires in Canada resulted in poor air quality in the financial capital of the world, New York in June this year along with smoke and insects in the air very similar to Delhi air in Oct-Dec period every year suggesting no place on earth is turning out to be better than the other for ordinary citizens of the world! I hope future generations will provide some leadership since the present one has failed so far. Have a happy reading!
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".