A rare intercomparison of nutrient analysis at sea: lessons learned and recommendations to enhance comparability of open-ocean nutrient data
Bibliographic record
Abstract
Abstract. An intercomparison study has been carried out on the analysis of inorganic nutrients at sea following the operation of two nutrient analysers simultaneously on the GO-SHIP A02 trans-Atlantic survey in May 2017. Both instruments were Skalar San++ Continuous Flow Analyzers, one from the Marine Institute, Ireland and the other from Dalhousie University, Canada, each operated by their own laboratory analysts following GO-SHIP guidelines while adopting their existing laboratory methods. There was high comparability between the two data sets and vertical profiles of nutrients also compared well with those collected in 1997 along the same A02 transect by the World Ocean Circulation Experiment. The largest differences between data sets were observed in the low-nutrient surface waters and results highlight the value of using three reference materials (low, middle and high concentration) to cover the full range of expected nutrients and identify bias and non-linearity in the calibrations. The intercomparison also raised some interesting questions on the comparison of nutrients analysed by different systems and a number of recommendations have been suggested that we feel will enhance the existing GO-SHIP guidelines to improve the comparability of global nutrient data sets. A key recommendation is for the specification of clearly defined data quality objectives for oceanic nutrient measurements and a flagging method for reported data that do not meet these criteria. The A02 nutrient data set is currently available at the National Oceanographic Data Centre of Ireland: https://doi.org/10.20393/CE49BC4C-91CC-41B9-A07F-D4E36B18B26F and https://doi.org/10.20393/EAD02A1F-AAB3-4F4E-AD60-6289B9585531.
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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 | 0.314 | 0.408 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.019 | 0.012 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".