Bibliographic record
Abstract
Series of meetings related to COPS-Bali, Egpyt, Montreal and may be in Delhi later this year, are all still debating the response to internationally acceptable limit to temperature increase.None now questions the increase but only as to how to manage it!In addition to the rise in temperature, world is increasingly facing the plastic problem to the extent that even rain water pours microplastics in New Zealand.In the meantime, on the health front while the world economy seems to be gradually opening up after more than two years of downhill slide, China-the starting point of the pandemic slow down still seem to face serious issues on the covid front with large infections reported around the country.This is a serious matter for the whole world since people are again travelling and thus are potential carriers of multitude of new variants of the virus.The winter blizzard in North America is an indication how nature reacts to climate changeunpredictable and unstoppable.We can all only wish for better days and senses to come regarding climate change drivers.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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.052 | 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; 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".