Cisco diversity in a historical drainage of glacial Lake Algonquin
Bibliographic record
Abstract
Cisco (Coregonus artedi (sensu lato) Lesueur, 1818) forms matching in appearance to Blackfin Cisco from the Laurentian Great Lakes occur in four lakes in Algonquin Park, Ontario, Canada, a historical drainage of glacial Lake Algonquin (precursor of lakes Michigan and Huron). Their occurrence may represent colonization from glacial Lake Algonquin drainage patterns 13 000 calibrated years BP or independent evolution within each lake. Gill-raker numbers, temperature at capture depth during lake stratification, and hurdle models of habitat distribution are summarized. Blackfin (nigripinnis-like) in the four lakes had higher gill-raker numbers than artedi-like cisco captured in nearby lakes or within the same lake. Two lakes have a bimodal gill-raker distribution that indicate co-occurrence of two forms. Blackfin occupied the hypolimnion with a peak depth distribution at 20–25 m. Maximum depth for blackfin was 35–40 m. The presence of the opossum shrimp (Mysis diluviana Audzijonyte and Väinölä, 2005) appears necessary for the occurrence of cisco diversity in lakes but not sufficient in all cases. The presence of two forms of cisco in at least two lakes points to the possibility of the colonization hypothesis or the ecological speciation hypothesis as accounting for this phenomenon. Genetic analysis is needed to determine which of these hypotheses best accounts for the occurrence of blackfin in Algonquin Park.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".