Magnitudes and controls of organic and inorganic carbon flux through a chain of hard‐water lakes on the northern Great Plains
Bibliographic record
Abstract
Whole‐lake carbon (C) mass‐balance budgets were constructed for a chain of six hard‐water lakes to quantify the relative importance of organic carbon (OC) and inorganic carbon (IC) exchanges between atmosphere, water column, sediments, and rivers. Mean summer C fluxes were calculated for each lake during the ice‐free periods (May to September) of 1995‐2007 by measuring deposition of IC and OC in lake sediments, export of C to outflow rivers, lotic C influxes, and atmospheric exchange of CO2. Unlike soft‐water lakes, IC in rivers accounted for 68.2‐85.6% of total C (TC) influx to these hard‐water lakes, CO2 efflux accounted for 0‐44.5% of total C export (median 2.8%), and sedimentation buried similar amounts of OC and IC. Deposition of C in sediments accounted for 1.8‐61.7% of total export and was correlated to water residence time, while C efflux through rivers accounted for 32.6‐98.2% of total export, mainly as IC (69.6‐85.1% of TC). Unexpectedly, estimates of net ecosystem production based on OC mass balances suggested that all lakes were autotrophic (production > respiration) during summer, despite elevated dissolved organic carbon content (5.6‐16.1 mg C L‐1), pCO2 values, and net CO2 emissions to the atmosphere from three lakes. We conclude that C fluxes within and among these hard‐water lakes are regulated by hydrologic inputs of dissolved IC rather than by lake metabolism, IC and OC pathways are only loosely coupled, and future climate variability will alter C fluxes in similar lakes mainly through regulation of mass transfer from land.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".