Bibliographic record
Abstract
By now we hold the ability to develop the submeter high resolution satellite. It is the outcome of continuous development supported by the government demand and upbringing policy. Korea started the sales of satellite images by KOMPSAT-1 in 2000 and made a full-scale attempt to sell the KOMPSAT-2 images in 2007. Despite such efforts, the business outcome has not been improved because of some entry barriers. We are now faced with the times that force us to enter the global market of satellite images for the better business outcome, using the past experience and domestic infrastructure. Unlike our case, the American, French, and Canadian companies develop the emerging market and occupy the 75% of world market. The business strategy of these advanced countries is that: 1) the government fosters the relevant business companies to strengthen the competitiveness and to equip the growth engine. 2) the company establishes the growth platform to develop the foreign market, while providing its government users with satellite images and services. Under the changing environment of global market of satellite images, we examine whether or not our paradigm with the emphasis on the public services is suitable for global marketing of our satellite images. As a first step to meet the demands of the times, we suggest the strategic approaches to enhance the export of satellite images.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.182 | 0.108 |
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".