Parsing the DOM sources using calibrated biomarkers in the San Francisco Bay Estuary
Bibliographic record
Abstract
The San Francisco Bay Estuary (SFBE) together with the Sacramento–San Joaquin River Delta is the second largest estuary in the United States and represents a highly dynamic ecosystem. From 2014 to 2016, we conducted three transects across a salinity gradient to investigate the roles of sources, hydrologic and seasonal changes on the DOM composition. Sampling started with a riverine endmember, through a vast area of marshes, wetlands, to the Golden Gate, the largest estuary in western North America. The winter transect at its maximum discharge allowed the study of DOM dynamics largely in the absence of photodegradation processes and low levels of algal production; the summer transect captured significant photodegradation and algal production; the spring transect revealed the signal of stored DOM from the snowmelt cold water flows. Multiple studies indicated that algal primary production alone cannot support the SFBE foodweb, and the wetlands could also serve to reduce DOM loadings coming off of the delta. Hence, other sources of organic matter must be considered, including autochthonous and allochthonous DOM. Terrestrial DOM export in SFBE were revealed by dissolved lignin dynamics. Optical proxies (UV-vis and fluorescence) were also used to study the photochemical and biological transformations of DOM.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 | 0.000 |
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".