Data and code from: A review of cisco form diversity and lake depth
Bibliographic record
Abstract
Cisco form diversity overlaps with glacial lake coverage in North America. A recent hypothesis proposes that secondary contact among refugial cisco lineages during retreat of the Laurentide Ice Sheet was the basis for the geography of cisco diversity in North American lakes. Glacial lake coverage in North America is also a map of Mysis diluviana distribution, a glacial relic species found in lakes including the Laurentian Great Lakes. The presence of Mysis increases isotopic niche size of cisco, a necessary condition for Mysis to be a key element of cisco clade diversification. We reviewed literature on cisco forms in North American lakes to determine if cisco diversity is related to an ecological opportunity gradient, maximum lake depth with the presence of Mysis. Cisco form diversity increases with lake maximum depth in North America in the presence of Mysis with deeper lakes increasingly likely to hold multiple forms. Lakes without Mysis had only one cisco form and were shallower in general. We hypothesize that Mysis is the basis for the relationship between lake depth and cisco form diversity because of the complex behavior and distribution of Mysis that stems from increasing lake depth. We propose the geography of cisco diversity in North America results from within lake predator–prey processes as a function of lake depth under the Mysis hypothesis vs. secondary contact priming genetic diversity. The two hypotheses are not mutually exclusive. Analysis of a lake set covering both cisco and Mysis ranges points to more lakes having multiple cisco forms.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.054 |
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".