Alkalinity distribution in the western North Atlantic Ocean margins
Bibliographic record
Abstract
Total alkalinity (TA) distribution and its relationship with salinity (S) along the western North Atlantic Ocean (wNAO) margins from the Labrador Sea to tropical areas are examined in this study. Based on the observed TA‐S patterns, the mixing processes that control alkalinity distribution in these areas can be categorized into a spectrum of patterns that are bracketed by two extreme mixing types, i.e., alongshore current‐dominated and river‐dominated. Alongshore current‐dominated mixing processes exhibit a segmented mixing line with a shared mid‐salinity end‐member. In such cases (i.e., Labrador Sea, Gulf of Maine, etc.), the y‐intercept of the high salinity segment of the mixing line is generally higher than the local river alkalinity values, and it reflects the mixing history of the alongshore current. In contrast, in river‐dominated mixing (Amazon River, Caribbean Sea, etc.), good linear relationships between alkalinity and salinity are generally observed, and the zero salinity intercepts of the TA‐S regressions roughly match those of the regional river alkalinity values. TA‐S mixing lines can be complicated by rapid changes in the river end‐member value and by another river nearby with a different TA value (e.g., Mississippi‐Atchafalaya/Gulf of Mexico). In the wNAO margins, regression intercepts and river end‐members have a clear latitudinal distribution pattern, increasing from a low of ∼300 μ mol kg −1 in the Amazon River plume to a high value between ∼500–1100 μ mol kg −1 in the middle and high latitude margins. The highest value of ∼2400 μ mol kg −1 is observed in the Mississippi River influenced areas. In addition to mixing control, biological processes such as calcification and benthic alkalinity production may also affect ocean margin alkalinity distribution. Therefore, deriving inorganic carbon system information in coastal oceans using alkalinity‐salinity relationships, in particular, those of generic nature, may lead to significant errors.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".