Exploring the Bacterial Preference for Terrestrial or Marine Organic Matter in Estuarine Sediments Using Compound Specific Stable Carbon Isotope Ratios: A Degradation Kinetics Study 
Bibliographic record
Abstract
Preserving the health of estuarine ecosystems has been an increasing challenge in the recent past with the spreading of areas affected by deep-water hypoxic conditions. Hence, it is of critical importance to identify the causes of such perturbation, triggered by changing ocean circulation and increasing inputs of organic matter (OM), which results in serious threats to living species. Estuaries are large deposition centers for organic matter (OM) where stable carbon isotope ratios of either bulk OM or specific organic compounds provide detailed information about carbon cycling and the tracing of OM sources and transformations along the terrestrial-marine continuum. In particular, the ∂13C values of biomarkers that are specific to heterotrophic bacteria (branched iso- and anteiso-C15:0 fatty acids) can be used to assess the type of OM that they preferentially degrade as the ∂13C values of marine organic carbon (OC) are more enriched in 13C than those of terrestrial OC. However, very little is known on the dynamics between the seasonally varying relative inputs of terrestrial vs. marine OM and the ∂13C values of these bacteria-specific fatty acids. In this study, we will use a kinetic batch incubation approach in which natural sediments from the St. Lawrence Estuary and Gulf, amended with fresh terrestrial or marine OM characterized by a very different 13C/12C ratio (difference of between 10 and 14 ‰ depending on the sampling station), will be incubated for varying amounts of time. Quenching of the incubations followed by the extraction, quantification and isotopic characterization of the bacterial fatty acids will allow determining the rate and temporal extent of change of their compound-specific ∂13C values. Bulk elemental (OC and total nitrogen) and isotopic (∂13C and ∂15N) mass balances will be precisely monitored throughout the experiment. Acquisition of this knowledge, combined with results from other studies carried out in our lab, will provide a better understanding of the relative importance of terrestrial and marine OM processing in the onset of hypoxia and will be exploited as a guide for remedial efforts aiming to improve the health of such an important ecosystem.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".