ПРОБЛЕМА ПОНИМАНИЯ И ИСПОЛЬЗОВАНИЯ ТЕРМИНОВ “ЕДОМА” И “ЛЕДОВЫЙ КОМПЛЕКС”
Bibliographic record
Abstract
Рассмотрена этимология и история развития научных терминов “едома” и “ледовый комплекс”, широко используемых учеными разных направлений при исследовании ландшафтов, геоморфологических условий, геологии четвертичных образований и истории криолитогенеза на Северо-Востоке Евразии. Обсуждаются проблемы в употреблении этих терминов, неточности и разночтения, зафиксированные в справочной и учебной литературе. С учетом новых данных о строении сильнольдистых отложений, полученных за последние четверть века, приведены новые основания для их уточнения и предложены их определения. Показано, что терминологические проблемы часто связаны с недоизученностью определяемых объектов, а также с небрежностью исследователей в использовании терминов и изложении своих результатов. The article examines the etymology and history of the evolution of the scientific terms “yedoma” and “ice complex”, widely used by scientists of different fields in the study of landscapes, geomorphological conditions, Quaternary geology and the history of cryolithogenesis in the North-East of Eurasia. Some problems in the use of these terms, inaccuracies and discrepancies recorded in reference and educational literature are considered. Based on new data on the structure of ice-rich deposits obtained over the past quarter of a century, new grounds for their clarification are given and their definitions are proposed. It is shown that terminological problems are often associated with under-exploration of the objects being defined, but to no lesser extent they are determined by the verbal carelessness of researchers in the presentation of their results.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.015 |
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".