Are Lesser Snow Geese, <em>Chen caerulescens caerulescens</em>, Exceeding the Carrying Capacity of the Fraser River Delta's Brackish Marshes?
Bibliographic record
Abstract
Brackish marshes of the Fraser River delta provide important habitats for such high-profile animals as White Sturgeon (Acipenser transmontanus), Pacific Eulachon (Thaleichthys pacificus), Pacific salmon (Oncorhynchus spp.), Western Sandpiper (Calidris mauri), and Lesser Snow Goose (Chen caerulescens caerulescens), the latter comprising the “Fraser-Skagit” segment of the Wrangel Island (Russia) population. This study assessed whether the current numbers of Snow Geese are exceeding the carrying capacity of brackish marshes in the Fraser River delta. Simulation modelling predicts that those marshes are presently capable of supporting ~17,500 Snow Geese—a value that is greatly exceeded by the numbers of geese that have over-wintered there in recent years (~80,000 in 2004-2005). The Pacific Flyway Council’s target 3-y average population and segment sizes of 120,000 and 50,000 - 70,000, respectively, were set without considering the carrying capacity of natural wintering habitats, the potential impacts of too many geese on upland agriculture, or implications for hazards to civilian aircraft at Vancouver International Airport. The modelled results of the present study suggest that the Fraser River delta can sustain the current numbers of Snow Geese that stage or winter there only if those birds also forage in agricultural and refuge fields—a relatively recent phenomenon that likely bolstered the Snow Goose population. Over-use by Snow Geese can degrade the productivity and habitat quality of marshes. There is documented evidence that some key plant species (e.g., Scirpus americanus) of the brackish marshes of the Fraser River delta are well below their biomass potential (~15%), primarily because of grubbing by Snow Geese. Other species that depend on this brackish environment as well as human interests in the Fraser River delta may be adversely affected by an overabundance of Snow Geese. The future effectiveness of hunting as a primary means of population regulation is questioned.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".