Biogeography of the deepwater sculpin (Myoxocephalus thompsonii), a Nearctic glacial relict
Bibliographic record
Abstract
Although the freshwater fish fauna of North America is relatively well studied, the biogeography of the deepwater sculpin ( Myoxocephalus thompsonii (Girard, 1851)) remains poorly understood. Collections of the species are limited, both because of its relatively remote distribution and because its habitat at the bottom of very deep lakes presents considerable logistic challenges for sampling. To investigate the biogeography of the deepwater sculpin, we conducted a range-wide (excluding the Laurentian Great Lakes) survey for the species between May and October 2004. Deepwater sculpin were collected using a variety of sampling gears, including a trap that was specifically designed to capture the species. We hypothesized that deepwater sculpin would be found only in areas that were formerly occupied by glacial lakes or the Champlain Sea. We reconstructed the historical boundaries of these water bodies and found that nearly all lakes where deepwater sculpin were collected, including four new localities, were within those limits. Conversely, the species was not detected in sampled lakes that were beyond these boundaries. Our results clarify the distribution and biogeography of the deepwater sculpin and strengthen the view that the current distribution of the species was mediated by dispersal through glacial lakes and the Champlain Sea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".