Demographic analyses of a Trumpteter Swan Cygnus cygnus buccinator population in Western USA
Bibliographic record
Abstract
Trumpeter Swan surveys have been conducted in the northern Rocky Mountain region of the U.S.A. since 1929. Survey timing and methods have varied. Two aerial censuses, in September and February, are currently used to monitor population size, recruitment and distribution of the Rocky Mountain Population, its two subpopulations, and several component flocks. The Tristate subpopulation increased from 69 trumpeter swans in 1932 to 627 in 1954. It then began to fluctuate widely around a mean of 536 swans. Despite the fluctuations, the slope of the Tristate subpopulation from 1950 to 1989 is not different than zero. The subpopulation currently numbers 565. Annual fluctuations are due primarily to variations in recruitment rates. Although numbers increased and fluctuated, flock distribution remains relatively stable. In September, Montana contains 65.6%, Idaho 11.4% and Wyoming 23.0% of the Tristate subpopulation. The Interior Canada subpopulation has increased from approximately 200 in 1974 to about 1,150 in 1989. Most of these migratory swans winter in Idaho. The increase in numbers has resulted in shifts in wintering distribution. Montana hosts more swans and a declining percentage of the total Rocky Mountain Population (35.6% ). Idaho winters more swans and an increasing percentage of the total (49.8%). Wyoming numbers have increased, but it continues to winter about the same percentage (12.5%) of the winter population. Shifts are due to over-crowding and/or forage depletion on traditional wintering sites, poor weather conditions, low water levels, varying ice conditions, and differential recruitment by flocks using specific traditional sites. The two seasonal censuses allow close monitoring of demographic patterns, evaluation of management procedures, and formulation of research needs.
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".