Quantity and quality of particulate organic matter controls bacterial production in the Columbia River estuary
Bibliographic record
Abstract
Abstract Estuaries function as “bioreactors” for fluvial materials in which microbial, biogeochemical, and ecological processes transform organic matter and nutrients prior to export to coastal oceans. The impact of estuarine bioreactors is linked to the bioavailability and residence time of fluvial material, and to rates of microbial activity. In the Columbia River estuary, water residence time is short (approximately 2 d), but particle residence time is extended by estuarine turbidity maxima (ETM). To investigate relationships between organic matter and microbial activity, samples were collected in spring and fall 2012 and summer 2013, and ETM particles were fractionated by settling velocity using an Owen‐style settling column. Data were also analyzed from 16 other sampling campaigns conducted in 1990–2009. The composition of suspended particulate matter shifted seasonally following the spring freshet and river phytoplankton bloom with decreasing organic content, increasing C/N ratio, and an increasing contribution of autochthonous particulate organic matter (POM) produced in four shallow lateral bays (based on del‐PO13C and pigment ratios). Heterotrophic bacterial production responded to seasonal changes in POM and correlated most strongly with estimates of labile particulate nitrogen during any particular season, and with the riverine flux of chlorophyll a (Chl a) across all seasons. Regression models suggest that labile particulate nitrogen and bacterial production can be predicted from sensor‐based measurements including turbidity, salinity, and temperature in the estuary and Chl a in the river. These results demonstrate that heterotrophic activity in the Columbia River estuary is controlled by POM lability, and by the degree to which ETM retain and concentrate POM.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".