Earth observation and geospatial big data management and engagement of stakeholders in Hungary to support the SDGs
Bibliographic record
Abstract
To support the monitoring and reporting processes during implementation of the Sustainable Development Goals, well-developed, commonly recognized Earth observations and geospatial data, methods, innovations, committed professionals, and strong sustainability policies are necessary.This article informs the readers on the Earth observation and geoinformation developments and innovations, and on the engagement of profession, academy and governance to support implementation of the Sustainable Development Goals in Hungary.Description, analyses and critical assessments are given on the elements selected from Hungarian sustainable-oriented Earth observation and geospatial novelties: (a) Working Group for Sustainable Development mission and national sustainability-policy, (b) international partnerships, domestic activities and achievements, (c) status of the professional education, (d) spatial databases and services to support implementation of the sustainable development, (e) a case study on the internationally recognized soil geoinformation system, (f) national Earth Observation Information System and perspectives of its applications for monitoring the sustainability.The article conclusion strongly advises the Hungarian realization of (a) institutionalization of the Earth observation and geospatial tools and capacity for sustainable development, (b) their use in integration with statistical data, (c) establishment of national spatial information infrastructure and (d) development and spreading of the use of big data.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".