Agrometeorological Observation Environment and Periodic Report of Korea Meteorological Administration: Current Status and Suggestions
Bibliographic record
Abstract
2011년 농업기상 관측장비의 재배치 사업 이후, 기상청의 농업기상 관측환경은 전반적으로 상당히 향상되었다. 본 연구는 최근 실시한 현지답사 결과를 바탕으로, 1) 확실한 관리주체의 확립, 2) 유관기관들 간 상호협조 강화, 3) 관측환경 변화에 대한 상세한 기록, 4) 장비와 센서의 표준화 5) 통일된 형태의 안내판 설치, 6) 부적합한 환경 하에 놓여 있는 장비의 인접 장소로의 이동, 7) 자동화된 증발계의 설치가 후속적으로 이루어져야 함을 제시하였다. 또한, 확보된 고품질의 자료를 활용하기 위하여 현재 중단되어 있는 농업기상 정기보고서의 제작이 필요하며, 우리 나라의 현황을 감안할 때 1년에 한 차례 국가 전체에 걸쳐 지난 1년 간 일어난 특징적인 농업기상 현상들을 시간 순으로 서술하는 독일이나 캐나다의 연례보고서를 참고할 것을 제안한다. Since the relocation project of equipment in 2011, the overall circumstances of KMA's agrometeorological observation have been significantly improved. Some concerns, however, emerged as a result of the evaluation of observational circumstances in terms of quality assurance after the field surveys on all stations. In order to improve the situation, we suggest: (1) establishment of clear management responsibilities, (2) enhancement of mutual cooperation system between relevant organizations, (3) detailed records of the changes in the observational circumstances, (4) standardization of equipment and sensors, (5) installation of unified information boards, (6) transfer of inappropriate facilities to an adjacent cropland and (7) setup of automated evaporation pan. In order to effectively utilize the high-quality data obtained through improvement of observational circumstances and an elaborate quality control, it is recommended to publish and disseminate regular reports on agrometeorological observations. To produce such a report on a trial basis, we have investigated different types of regular reports issued by domestic and foreign organizations, publication periods, geographical scope, main contents and amount. Based on our current situation, it would be beneficial to learn from the cases of Germany and Canada, which summarize mainly the distinctive agrometeorological phenomena occurred over the past years across the country.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".