Controls of δ<sup>13</sup>C‐DIC in lakes: Geochemistry, lake metabolism, and morphometry
Bibliographic record
Abstract
We investigated δ13 carbon (C)‐dissolved inorganic carbon (DIC) values in 72 lakes from diverse regions using literature data as well as new measurements for 32 lakes. δ13C‐DIC varied broadly among lakes from ~31 to +2.6‰. This variation of surface‐water δ13C‐DIC among lakes is greater than the seasonal variation within most lakes. Several statistical models account for a large portion of the interlake variation and indicate that geochemical (e.g., DIC, pH, alkalinity) and morphometric (area) variables are important, whereas biological (e.g., gross primary productivity [GPP], respiration [R], chlorophyll a) variables are generally not significant. A process‐based model including gas exchange with the atmosphere, inorganic carbon speciation, and ecosystem metabolism was also constructed. The model provides a reasonable fit to the data for lakes, in which respiration exceeded GPP (heterotrophic lakes; 75% of lakes sampled). Lakes for which GPP exceeded respiration (autotrophic) were not fit well by the process‐based model. The data and models indicate that metabolism creates substantial variation in δ13C‐DIC around the potential δ13C‐DIC that is set by geochemical factors of the watershed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".