A system to quantitatively recover bacterioplankton respiratory CO<sub>2</sub>for isotopic analysis to trace sources and ages of organic matter consumed in freshwaters
Bibliographic record
Abstract
We present a new system and method to measure the natural abundance isotopic (δ13C, Δ14C) values of respiratory CO2 produced by bacterioplankton, to directly assess the sources and ages of organic carbon (OC) respired by bacteria in aquatic ecosystems. The Respiratory Carbon Recovery System (ReCReS) and operating procedure were designed to reduce background dissolved inorganic carbon values by > 98% and then quantitatively recover the CO2 derived from bacterial respiration after incubation of freshwater samples. The 2-component ReCReS consists of an airtight incubation system (20 L), for short-term regrowth incubations of filtered water samples inoculated with ambient bacteria, and a harvest system to recover the respiratory CO2 produced during these incubations. The multistep operating procedure involves the following: (1) filling of incubation system with 0.2-µm filtered sample water; (2) addition of 1 N HCl (pH to ~2.8); (3) sparge with ultrahigh-purity (UHP) He to remove dissolved inorganic carbon; (4) sparge with UHP, volatile organic carbon-, CO2-free air to replenish oxygen; (5) neutralization (1 N carbonate free NaOH), reinoculation with the ambient bacterial assemblage, and incubation (80-132 h); (6) acidification to pH 2.8; and (7) UHP He sparge for > 12 h. The evolved CO2 is sent through 2 water traps (dry ice slurries) before cryogenic trapping in liquid N2. Control incubations were processed concurrently to evaluate extraction efficiencies, potential methodological contamination, and fractionation artifacts. Collectively, our results suggest that respiratory CO2 is quantitatively recovered and the isotopic fidelity (δ13C, Δ14C) between the OC respired and the CO2 harvested is retained. Moreover, the system allows the recovery of sufficient C to measure both δ13C and Δ14C values, even in the most oligotrophic systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".