Reconciliation of total particulate organic carbon and nitrogen measurements determined using contrasting methods in the North Pacific Ocean as part of the NASA EXPORTS field campaign
Bibliographic record
Abstract
Measurements of particulate organic carbon (POC) are critical for understanding the ocean carbon cycle, including biogenic particle formation and removal processes, and for constraining models of carbon cycling at local, regional, and global scales. Despite the importance and ubiquity of POC measurements, discrepancies in methods across platforms and users, necessary to accommodate a multitude of needs and logistical constraints, commonly result in disparate results. Considerations of filter type and pore size, sample volume, collection method, and contamination sources underscore the potential for dissimilar measurements of the same variable assessed using similar and different approaches. During the NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) 2018 field campaign in the North Pacific Ocean, multiple methodologies and sampling approaches for determining POC were applied, including surface inline flow-through systems and depth profiles using Niskin bottles, in situ pumps, and Marine Snow Catchers. A comparison of results from each approach and platform often resulted in significant differences. Supporting measurements, however, provided the means to normalize results across datasets. Using knowledge of contrasting protocols and synchronous or near-synchronous measurements of associated environmental variables, we were able to reconcile dataset differences to account for undersampling of some particle types and sizes, possible sample contamination and blank corrections. These efforts resulted in measurement agreement between initially contrasting datasets and insights on long-acknowledged but rarely resolved discrepancies among contrasting methods for assessing POC concentrations in the ocean.
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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 | 0.000 | 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 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".