Bibliographic record
Abstract
Among all the challenges before the human being the climate change is one of major challenge. As due to climate change not only the flora and fauna is endangered but also the existence of human being. Due to the climate change there is increase in the temperature of the earth, rise in sea level, rise in frequency of Hurricane/Cyclone, increase in displacement of people, increase in food Insecurity, which ultimately causing danger to human existence. Diseases like Lung cancer, Malaria, Dengue, skin infections, water born diseases etc. are also the results of climate change. So, from many years climate change has become the global challenge and various principles have been made in various declarations, treaties or conventions like Stockholm declaration, Montreal Protocol, Brundtland commission, Earth Summit which also includes agenda 21 and convention on climate change and biodiversity, United Nations framework on climate change in which conference of party is being held in each and every year, Paris Agreement, Agenda 2030 etc. Therefore, from above it is clear that a lot of actions to combat the climate change have been taken at the global and consequently at national level but keeping in mind the current climate emergency, now it's time to take the individual actions too, in addition to global and national actions and policies. This paper, in addition to the global action taken, suggest some individual's new and innovative ideas (on the basis of survey) to combat the climate change and its impact.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".