Deconvolution of the Labrador Sea Transit-Time Distribution from Combined Measurements of CFC-11 and CFC-12
Bibliographic record
Abstract
The Labrador Sea is an important deep water formation site where a large fraction of theocean's deep waters had their last contact with the atmosphere. Consequently, quantifying the ventilation of the Labrador Sea is crucial for understanding the global ocean circulation. In this study, I use yearly hydrographic measurements of CFC-11 and CFC-12 conducted along WOCE repeat section AR7W from 1991 to 2009. I present improvements to previous application of Bayesian evidence framework to 3-dimensional interpolation of CFC-11, CFC-12, temperature and salinity data. The Bayesian evidence framework is also used to infer the Labrador Sea's climatological and interannual transit-time distribution (TTD) with a maximum entropy deconvolution method. We present improvements to the ventilation problem to allow for measurement errors and to better quantify the uncertainty in the estimated TTDs. These improvements include the introduction of an intrinsic correlation function to explicitly take into account correlations in adjacent portions of the TTD and the choice of hyperparameters in our model. Our results provide a baseline from which changes in theventilation of the Labrador Sea can be quantified.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".