Bibliographic record
Abstract
The 2021 2nd International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2021) was be held virtually online on April 23-25, 2021 due to the precaution taken to minimizing the COVID-19 risk. The safety and well-being of all conference participants was our first and top priority, while we strived to offer many scholars and researchers this long-awaited conference to conduct academic exchanges with their peers. ICGMRS 2021 is to bring together innovative academics and industrial experts in the field of geology, mapping and remote sensing to a common conference. The primary goal of the conference is to promote research and developmental activities in geology, mapping and remote sensing and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The COVID-19 virus has made our life very challenging, but we want to reiterate that there are no barriers to science, as we continue to do our research works via modern technical means. The ICGMRS 2021 has selected Zoom as the virtual platform. Each presenter will be given a 15-minute talk and followed by a short discussion afterward. Before the conference, all of the authors were encouraged to submit a video as a backup in case of unexpected technical problems. There were 140 individuals who attended this on-line conference, represented many countries including China, Canada, South Korea, Singapore and UK. During the conference, we invited three professors as our keynote speakers. A. Prof. Chao Chen, from Zhejiang Ocean University, performed a speech: Spatio-temporal pattern evolution of coastlines for archipelagic regions. His research area is Marine Environment Remote Sensing. And then we had A. Prof. Heng Dong, from Wuhan University of Technology. He delivered a speech: Estimation of Global Terrestrial Gross Primary Productivity based on Solar-Induced Chlorophyll Fluorescence. In this study, after analyzing the fluorescence emission mechanism at different spatial scales, and the GPP-SIF empirical linear estimation model, some factors affecting the photosynthetic capacity of the vegetation and the canopy SIF emission were introduced to construct a new GPP estimation method. Lastly, we were glad to invite A. Prof. Xuemin Xing, from Changsha University of Science & Technology as our finale keynote speakers. She shared a speech: Measuring subsidence over soft clay highway based on a novel time-series InSAR deformation model: with emphasis on rheological properties and seasonal factors. Their insightful speeches had triggered heated discussion of the conference. Every participant praised this conference for disseminating useful and insightful knowledge. The proceedings are a compilation of the accepted papers and represent an interesting outcome of the conference. Topics include but are not limited to the following areas: Geography & Geology, Surveying & Mapping, Remote Sensing, Application of Remote Sensing Technology and other related topics. All the papers have been through rigorous review and process to meet the requirements of International publication standard. We would like to acknowledge all of those who supported ICGMRS 2021. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. The Committee of ICGMRS 2021 Committee member, Conference Chair, Program Committees, Technical Program Committees and this titles are available in this pdf.
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 | 0.002 | 0.011 |
| 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 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.549 | 0.404 |
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".