Bibliographic record
Abstract
The 2025 5th International Conference on Computer, Remote Sensing and Aerospace (CRSA 2025) was convened in Jinan, China, from August 22 to 24, 2025. Since its inception, the CRSA conference series has been dedicated to promoting international exchange and collaboration in cutting-edge fields such as computer science, remote sensing technology, and aerospace engineering, establishing itself as a highly influential academic platform in these domains. Building upon the successful experiences and academic traditions of the previous four conferences, CRSA 2025 focused on the latest findings, technological breakthroughs, and future directions in mechanical, aerospace and automotive engineering. The conference aimed to provide an open, inclusive, and efficient environment for leading academics, researchers, and industry professionals to engage in in-depth dialogue and collaboration. In the context of rapid technological advancement, interdisciplinary integration has become a key driver of innovation in areas such as intelligent sensing, image processing, and manned/unmanned aviation. CRSA 2025 centered on these hot topics through diverse formats, including keynote speeches delivered by renowned experts: • Prof. Guisong Xia from Wuhan University, China: “AI4Geo: from Image Measurement to Geo-spatial Intelligence”. • Prof. Bin Zou from Central South University, China: “Practical Applications and Prospects of Remote Sensing for Heavy Metal Pollution Monitoring”. • Prof. Qingsheng Zeng from Universite du Quebec en Outaouais (UQO), Canada: “Analysis and Design of Lightweight, High-Efficiency, and Circularly Polarized Antennas for Satellite Platforms”. • Prof. Lu Leng from Nanchang Hangkong University, China: “Some New Findings about Security and Privacy of AI Systems”. List of Committee Member is 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 | 0.008 |
| 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) | 0.548 | 0.392 |
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".