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Record W204766924

BC bear viewing: an analysis of bear-human interactions, economic and social dimensions with recommendations for best practices

2001· other· en· W204766924 on OpenAlexaboutno aff
Owen T. Nevin, Bethany L P Gilbert

Bibliographic record

VenueInsight (University of Cumbria) · 2001
Typeother
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zoneGeographyFloodplainFish migrationOncorhynchusEcologyPopulationProductivityFisheryHabitatFish <Actinopterygii>Biology
DOInot available

Abstract

fetched live from OpenAlex

The Pacific mid-coast region of British Columbia has a mild, hypermaritime climate that places its biological productivity in the range of tropical rainforests. The low elevation river valleys are characterized by rich alluvial soils, further enriched annually by upstream nutrients flooding over the stream banks of the floodplains and distributing rich silt to the roots of giant Sitka spruce and Western hemlock forests. Unique to Canada's rivers flowing into the Pacific (but not north into the Mackenzie River, for example) are the massive contributions of nutrients from the bodies of 5 species of anadromous salmonids. This flux of organic matter has long been recognized as essential to the production of young salmon but the additional fertility increment to riparian and upland forests is currently under intense investigation (Bilby et al. 1996, Cederholm et al. 1999, Willson et al. 1998). The crucial role of migratory salmon in supporting dense populations of grizzly bears has recently been demonstrated for a large sample of coastal bears in Alaska (Miller et al. 1997). A strong statistical correlation between the per cent of meat, mainly salmon, in the diet and bear density (Hilderbrand et al. 1999) confirmed earlier speculation by Miller et al. (1997) that Alaskan's most dense bear populations also had high salmon diets and were among the most dense on a worldwide basis. Grizzly or brown bears on the coast of British Columbia and Alaska are the same species as the grizzly bears of the Rocky Mountains. However they are much bigger and have higher population densities because of abundant salmon (Hilderbrand et al. 1999). Alaskan population densities vary from a maximum of 550 bears /1000 km2 in Katmai National Park where salmon are seasonally available to less than 5 bears /1000 km2 for mountain bears of the eastern Brooks Range on a marginal food base (Miller et al. 1997). Coastal Alaskan bears forage widely for fish. At Brooks River in Katmai National Park & Preserve bears feed on sockeye salmon starting in late June as soon as they enter rivers to spawn (Gilbert 1995). At this time, when the salmon are rich in fat, a fuel used to ascend rivers, build redds, mate and defend their nests against others, hundreds of bears have daily access to the fish. Bears feed on these salmon which have 50% of their caloric value in fat. From Katmai's Brooks falls bears migrate with the fish to their spawning beds and, later, back to the stream mouths where the dying fish are again consumed in prodigious numbers. The end result of this movement is a pattern of deposition of fish pieces and feces over the landscape. Studies of the fate of salmon carcasses in the state of Washington showed that 22 species of mammals and birds carried salmon pieces into the forest (Cederholm et al. 1989). The nitrogen in the fish parts and bear feces and urine is incorporated into plants and animals in the forest and in the streams thereby enriching the ecosystems there. Bears are one of the largest contributors because of the massive amount of material that they consume and the great distances that they move. Many of the Alaskan sites with the highest bear densities have become popular, and profitable, tourist destinations. More recently a bear viewing/eco-tourist industry has begun to develop in British Columbia. In March 1998 bear viewing policy and guidelines were presented in which the government expressed support for the use of bears for viewing. This study addresses the impacts of viewing on bears and presents recommendations for further research and the sustainable development of bear viewing in the province.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.009
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.003

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.

Opus teacher head0.051
GPT teacher head0.283
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2001
Admission routes1
Has abstractyes

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