Chemical Oceanographic Studies of Water Mass Modification and Nutrient Transport in the Arctic and Subarctic Regions
Bibliographic record
Abstract
化学トレーサーを用いて，オホーツク海および北極海における海水の変質・混合過程を調べた筆者のこれまでの研究を紹介する。海水の酸素同位体比から，オホーツク海中層に海氷生成の影響を受けた水が広がっており，中層水に含まれるこの水の割合は20%であることが分かつた。CFCsの分布からは，海氷生成と潮汐混合が中層の通気に重要であることが 示された。さらに，中層水の水温・塩分には海氷生成よりも潮汐混合の影響がより大きいと見積もられた。 過去の酸素同位体比とアルカリ度のデータを用いて，北極海全域における融氷水/ブラインとその他の淡水を識別し，これらの分布を示した。さらに，北極海の中でも特に淡水が多いカナダ海盆の淡水収支を，栄養塩と酸素同位体比を用いて明らかにした。また，この際に水塊識別のトレーサーとして用いた海水中の窒素とリンの関係に着目し，北極海経由の水輸送が，海洋の窒素収支をバランスさせるための働きをしているという説を提唱した。
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".