Does the Natural DIC Affect the Storage of Total Inorganic Carbon in the Central Labrador Sea?
Bibliographic record
Abstract
The Labrador Sea plays a central role in the oceanic storage of carbon. In particular, several studies have shown that this region has amongst the highest integrated column inventories of anthropogenic carbon (Cant) in the world’s ocean. The rate at which Cant is stored in this region appears to be connected to changes in ocean circulation and can therefore vary over time. Nevertheless, it is still unclear whether the temporal variability of the total Dissolved Inorganic Carbon (DIC) inventory is solely due to the changes in Cant concentrations or whether there is a contribution of the natural component of DIC to this signal. The Bedford Institute of Oceanography has been maintaining the Atlantic Zone Off-Shore Monitoring Program (AZOMP) in the Labrador Sea since the early 1990s. The AZOMP involves annual occupations of the AR7W line that crosses the Labrador Sea and includes sampling of DIC, as well as multiple transient tracers such as CFC-12 and SF6. By using observations of DIC along the AR7W line, as well as previous estimates of Cant obtained with transient tracers (using a refined version of the Transit Time Distribution method; TTD) and new estimates of Cant based on the extended Multiple Linear Regression (eMLR) method, we provide a first insight on the role that the natural component of DIC plays in the temporal variability of inorganic carbon in the central Labrador Sea between 1993 and 2016. We show that different methods to estimate Cant can lead to different conclusions on the role of the natural variability of DIC and that these discrepancies could be related to the assumptions implied in the Cant estimation methods. In particular an analysis of Cant estimates obtained with our refined version of the TTD method in different water masses, highlighted that further refinement of the tracers’ saturation assumption could be necessary in this region. This refinement could reconcile the Cant estimates from the two methods and therefore lead to an unambiguous role of the natural DIC in this region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".