Interannual Variability of Fluorescent Dissolved Organic Matter Composition in the Canada Basin, Arctic Ocean From 2007 to 2017
Bibliographic record
Abstract
Abstract The interannual variability in fluorescent dissolved organic matter (FDOM) was assessed in the Pacific‐ and Atlantic‐derived halocline, and Atlantic waters (AWs) of the Canada Basin during late summer/early fall expeditions in 2007–2017. Fluorescence spectroscopy excitation‐emission matrices coupled with parallel factor analysis were used to validate a seven‐component model. During the 11 years, humic‐like intensities increased by 0.0002–0.0006 r.u. (Raman unit) (p < 0.05) with greater annual rates of change in shallower waters (i.e., Pacific summer water) than in deeper waters (i.e., AWs). No significant temporal trends were observed for protein‐like intensities in any water layer (p > 0.05) due to the labile nature of these components. The increases in humic‐like fluorescence intensities were likely the results of changes in Bering Strait inflow and its modification over the Chukchi Shelf and/or the accumulation of freshwater under anticyclonic wind forcing in the Beaufort gyre. This 11‐year late summer/early fall survey shows the first evidence of changes in FDOM composition in the halocline of the Arctic Ocean basin.
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 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".