Magical spherical ice (ice balls, ice eggs)
Bibliographic record
Abstract
大量球状冰集聚排列是自然界中较为罕见的现象，一般发生在浅滩、湖岸和河岸处.因球状冰形态特征的特殊性，常被称之为冰球、冰蛋.球状冰的形成与发展受气象、水动力和水滨地形条件等多因素共同控制，且具有一定的时空限制，必须在短时间内多因素协同干预才可能引发冰球集聚.正是凭借“制造”条件的苛刻性导致了冰球集聚现象的罕见，也造成了全球各地冰球出现的位置、形态和数量之间存在差异.已有来自于德国、俄罗斯、芬兰和加拿大等多个国家关于冰球现象的报道，但发生频率极少，约20~30年一次.近年来在吉林省的查干湖和四海湖发现了冰蛋现象，但关于球状冰从形成到大量集聚之间的定量研究依然缺少实测数据分析支撑.毫无疑问，来自大自然的神奇现象为科学探索研究提供了更多的动力和乐趣.;A large number of spherical ice accumulations are relatively rare in nature, and generally occur in shallows, lake banks and river banks. Due to the particularity of the morphological characteristics of spherical ice, it is often called ice balls or ice eggs. The formation and development of spherical ice are jointly controlled by meteorological, hydrodynamic and waterfront topographic conditions, and have certain spatial and temporal constraints. Multi-factor synergistic intervention in a short period of time is required to initiate ice balls to aggregate. Those rigorous conditions leads to the rarity of ice balls agglomeration, as well as the differences in the location, shape and number of ice balls pucks around the world. Up to now, there have been several reports on the ice balls phenomenon from Germany, Russia, Finland, Canada and other countries, whereas the frequency is very rare, about once every 20 to 30 years. In recent years, the ice egg phenomenon has also been discovered in Lake Chagan and Lake Sihai in Jilin Province, China, but the quantitative research on the formation of spherical ice from the formation to mass accumulation still lacks supporting measured data in situ. There is no doubt that the magical phenomena from nature provide more motivation and fun for scientific exploration and research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".