Quantifying lake allochthonous organic carbon budgets using a simple equilibrium model
Bibliographic record
Abstract
We quantify the allochthonous organic carbon (OC) budgets for seven north temperate lakes, using diverse information about their land cover, hydrology, and limnological characteristics. We develop a simple equilibrium model within a Bayesian framework that exploits the differences among the lakes to estimate three key rates: aerial loading (AOC) and wetland loading (WOC) from adjacent ecosystems and whole‐lake mineralization of OC (RDOC). Combined with observational data, these rates allow for estimates of the total OC loads, mineralization, and sedimentation within lakes and export to downstream ecosystems. AOC was 1.15 g C m−1 (shoreline) d−1, WOC ranged from 0.72 to 3.00 g C m−1 (shoreline) d−1, and RDOC, normalized to 20°C, ranged from 0.00083 to 0.0015 d−1. Total loads ranged from about 5 to 55 g C m−2 yr−1. Ecosystems immediately adjacent to lakes accounted for one‐half or more of total OC loads for some lakes. Whether a lake processed and stored more allochthonous OC than it exported depended primarily on hydrologic residence time. Our equilibrium model provides a parsimonious approach to quantifying allochthonous OC budgets in lakes with relatively minimal baseline data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".