Bibliographic record
Abstract
Climate change is a global problem promoted by global warming. It leads to uncertain climatic conditions such as floods, droughts, landslides, high waves, and sea level rise. It also resulting into extreme changes in Biodiversity, including the emerge of new variety of microorganism, including viruses. It significantly contributes in resulting the economic casualties and ecological losses. The new coronavirus (Covid-19) outbreak is also supposed to be the impact of climate change, where the mutation of the coronavirus species occurred due to the extreme change in the surrounding environment. Climate change impact is not only disease outbreak, disasters such as drought, flood, extreme heat and others are also among the threats. Those are resulted by the increasing greenhouse gases emission by human activities. We acknowledge to all the speakers at The 5th ICCC 2020, i.e.: Prof. Dr. Sutarno from Sebelas Maret University, Indonesia; Dr. Agung Suryawan Wiranatha from Udayana University, Indonesia; Dr. Takashi S.T. Tanaka from Gifu Univ., Japan; Dr. James MacGregor from World Planet, Canada; Prof. Dr. M. Nasir Uddin from Texas A&M Univ., USA; Dr. Emmy Latifah from UNS, Indonesia; Prof. Dr. Sanjib K. Panda from Rajashtan University, India; Dr. Mahawan Karuniasa, Member of PCCB, UNFCCC; Chairman of Indonesia Expert Network for Climate Change and Forestry, APIKI and Lecturer in University of Indonesia. We also express our highest gratitude to the Guest Editors who assisted in reviewing the papers, including Prof. Dr. MTh. Sri Budiastuti from Sebelas Maret University, Indonesia; Dr. Keigo Noda from Gifu University Japan; Dr. James MacGregor from Ecoplanet, Canada; Dr. Anthony Kent from RMIT Univ., Australia, and other reviewers we cannot mention one by one.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.553 | 0.357 |
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".