Small lakes dominate a random sample of regional lake characteristics
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 μEq 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.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".