Bibliographic record
Abstract
We are pleased to formally welcome each and every delegate to the 4th International Conference on Global Issues for Infrastructure, Environment, and Socio-Economic Development (4th GIESED) on behalf of the organizing committee and the conference chair. The conference is an annual event curated by the Graduate School, Hasanuddin University, and supported by the Hasanuddin University Makassar. The main goal of the conference is to facilitate scholarly communications and the sharing of information and concepts among experts and academics in the fields of infrastructure, environment, and socio-economic development, which include current topics like climate change, the use of renewable resources, and sustainable development. The opportunity for graduate students to present their research in front of professionals from academia and industry for scholarly feedback is the other major goal of the 4th GIESED. Additionally, it offers the chance for industry professionals and academics from universities to interact with the technical community, exchange experiences, and gain knowledge from the most recent technological advancements discussed at the conference. We sincerely hope that the 4th GIESED in 2021 offers an avenue for all delegates to engage in fruitful, useful, and useful discussions. This year’s theme is “Fostering Growth and Resilience in Science and Technology Innovation to Address Covid-19 Pandemic and Climate Change Crisis”. We would like to also express our gratitude to our invited speakers: Dr Peter Davey (Griffith University, Australia), Tim Hatch MPA (Alabama Disaster Agency, USA), Dr Michelle Peach (University of Rhode Island, USA), Thijs Sablerolle (Nexif Energy, Singapore), Prof Matsue Naoto (Ehime University, Japan), Prof Dadang Suriamiharja (Hasanuddin University, Indonesia), Dr Henri Bastaman (Ministry of Environment and Forestry, Indonesia), and Dr M V Reddy (Noveau Monde Graphite, Canada). Your attendance at our conference is truly an honor for us. We invited the participants to submit their manuscripts under the following major topics: infrastructure, environment, innovation, sustainable development, and renewable energy in order to further support this year’s theme. We are delighted to report that approximately 140 manuscript submissions were made through our submission system. We would like to congratulate 84 manuscripts that the board of reviewers determined to meet the scholarly standard after all manuscripts underwent a rigorous selection and peer-review process. List of Committees are available in this pdf.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.529 | 0.377 |
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".