Small lakes dominate a random sample of regional lake characteristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary 1. Lakes are a prominent feature of the Northern Highland Lake District (NHLD) of Wisconsin, covering 13% of the landscape. Summarising the physical, chemical, or biological nature of NHLD lakes at a regional scale requires a representative sample of the full size distributions of lakes. In this study, we selected at random 168 lakes from the full size distribution of lakes in the NHLD and sampled each lake for a broad suite of limnological variables. 2. Most lakes were small. The median lake area was 1.1 ha, however, half of the surface area of water was in a relatively small number of lakes larger than 162 ha. Smaller lakes tended to be low in dissolved inorganic carbon (DIC) and high in dissolved organic carbon (DOC). Inclusion of small lakes (<4 ha) in the survey resulted in an acid neutralising capacity (ANC) median (76.5 μ Eq L −1 ) much lower than previous estimates, and a DOC median (10.1 mg L −1 ) about 50% higher than it would have been without the smaller lakes. Unlike DOC, total P tended to be evenly distributed across lake sizes. 3. The implications of these findings are that regional summaries of lake characteristics for the NHLD are influenced by the inclusion of small lakes in the sample, even though most of the water surface area is in lakes larger than 162 ha. Excluding small lakes introduces bias in the estimates of organic carbon and inorganic carbon values, for example. Similar biases may be introduced for lake characteristics at the global scale if small lakes are not sampled, because the size distribution of lakes globally is dominated in number by small lakes.
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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 | 0.001 | 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) | 0.003 | 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 it