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Record W1533574939 · doi:10.22230/jem.2007v8n3a375

Old forest remnants contribute to sustaining biodiversity: The case of the Albert River valley

2007· article· en· W1533574939 on OpenAlexaff
Isabelle Houde, Susan Leech, Fred L. Bunnell, Toby Spribille, Curtis R. Björk

Bibliographic record

VenueJournal of Ecosystems and Management · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsUniversity of British Columbia
FundersMinistry of Environment
KeywordsBiodiversityGeographyLichenOld-growth forestTsugaWestern HemlockArcticVascular plantEcologyEcosystemThreatened speciesForestryHabitatSpecies richnessBiology

Abstract

fetched live from OpenAlex

The Albert River valley hosts the only old-growth stands of western redcedar in the Invermere Timber Supply Area (TSA). This portion of the Interior Cedar Hemlock moist cool (ICHmk1) biogeoclimatic variant is spatially disjunct from the rest of the ICHmk1 in British Columbia and lies on calcareous soil. Surveys of lichens and vascular plants in the valley bottom of the Albert River revealed an uncommonly rich area, including about 10% of the vascular plant species known to British Columbia. Eight of these are either Blue- or Red-listed in the province. Nine of the lichens found are either new to North America, western North America, or British Columbia, and seven may be new to science. Four more species have a predominantly oceanic distribution, and one is mainly Arctic. Conserving remnants of old-growth forest from forest harvest can play a critical role in sustaining biodiversity, particularly those in rare and poorly represented ecosystem types, so these areas merit careful consideration in the designation of reserves. Such significant remnants are easily overlooked when assessment of potential conservation areas is restricted to coarse-scale approaches that focus on intact landscapes. Coarse-filter approaches can identify potential rare ecosystems and guide field surveys, but are no substitute for field surveys.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.223
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.209
Teacher spread0.196 · 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 teacher head, 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
Published2007
Admission routes1
Has abstractyes

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