High Temporal Variability of Total Organic Carbon in the Deep Northeastern Pacific
Bibliographic record
Abstract
The interseasonal and interannual variability of total organic carbon (TOC) was assessed in the deep northeastern Pacific Ocean, an area characterized by high primary production and organic matter export. Samples were collected from throughout the deeper water column (>250 m) seasonally in 2017 and 2018 along the Line P transect, as well as one distribution of dissolved organic carbon (DOC) in spring 2018. High heterogeneity was observed in TOC concentrations at depths greater than 1000 meters, both within seasons (concentration differences of ~2-6 µmol kg-1) and across seasons (~2-12 µmol kg-1), coinciding with changes in fluorescence in the overlying waters. Such observations suggest that biogenic particles sinking from the upper ocean are seasonally delivering observable TOC to depth. The presence of these particles also appeared to contribute to the DOC pool, as suggested by differences in the TOC and DOC distributions in spring 2018. Seasonal TOC net accumulation and removal rates differed between the years: 0.5 and 2.1 µmol kg-1 day-1 (accumulation) and 0.6 and 2.1 µmol kg-1 day-1 (removal) for 2017 and 2018, respectively. The rate estimates indicated that introduction of organic carbon to the bathypelagic occurred at approximately the same rate as removal post-bloom, demonstrating the efficient removal of seasonally-produced organic carbon. High abundances of gelatinous zooplankton in spring 2018, supported by higher abundances of phytoplankton, enhanced the export of organic carbon to the bathypelagic zone during the seasonal bloom, resulting in localized TOC concentrations up to 148 µmol kg-1 in the bathypelagic. These results indicate high variability in bathypelagic TOC concentrations at high latitude, unlike oligotrophic systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".