Regional‐scale variation of dissolved organic carbon concentrations in Swedish lakes
Bibliographic record
Abstract
We assessed spatial variability in dissolved organic carbon (DOC) concentrations measured in nearly 2000 Swedish lakes. Inter‐lake variance peaked at two different scales, representing within‐region and between‐region variability. The variation between regions was greater than the variation among lakes within regions. We tested relationships between DOC and runoff, drainage ratio, and altitude for spatial heterogeneity using geographically weighted regression. Relationships varied geographically, but cluster analysis delineated two contiguous regions of similar relationships. Altitude had a significant inverse relationship with DOC in the highlands, and drainage ratio had a significant positive relationship with DOC in the lowlands. These heterogeneous relationships explained regional patterns in DOC concentrations. We conclude that regions, rather than individual lakes, are a key, emergent scale of spatial variability for DOC concentrations. This scale of variability reflects the intersection of environmental gradients (e.g., altitude) with spatially heterogeneous relationships (e.g., DOC—drainage ratio relationship). Regional‐scale structure in limnological patterns indicates that individual lakes are not independent from one another, but are emergent groups where DOC concentrations are a function of similar environmental patterns and processes.
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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 | 0.000 | 0.000 |
| 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".