Bibliographic record
Abstract
Abstract. This paper provides an overview and demonstration of emerging float-based methods for quantifying gross primary production (GPP) and net community production (NCP) using Biogeochemical-Argo (BGC-Argo) float data. Recent publications have described GPP methods that are based on the detection of diurnal oscillations in upper ocean oxygen or particulate organic carbon concentrations using single profilers or a composite of BGC-Argo floats. NCP methods rely on budget calculations to partition observed tracer variations into physical or biological processes occurring over timescales greater than one day. Presently, multi-year NCP time-series are feasible at near-weekly resolution, using consecutive or simultaneous float deployments at local scales. Results, however, are sensitive to the choice of tracer used in the budget calculations and uncertainties in the budget parametrizations employed across different NCP approaches. Decadal, basin-wide GPP calculations are currently achievable using data compiled from the entire BGC-Argo array, but finer spatial and temporal resolution requires more float deployments to construct diurnal tracer curves. A projected, global BGC-Argo array of 1000 floats should be sufficient to attain annual GPP estimates at 10-degree latitudinal resolution, if floats profile at off-integer intervals (e.g., 5.2 or 10.2 days). Addressing the current limitations of float-based methods should enable enhanced spatial and temporal coverage of marine GPP and NCP measurements, facilitating global-scale determinations of the carbon export potential, training of satellite primary production algorithms, and evaluations of biogeochemical numerical models.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.165 | 0.132 |
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".