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 (A OC ) and wetland loading (W OC ) 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. A OC was 1.15 g C m −1 (shoreline) d −1 , W OC 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".