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Record W2766528550 · doi:10.5558/tfc2017-029

Examining the role of terrestrial lichen transplants in restoring woodland caribou winter habitat

2017· article· en· W2766528550 on OpenAlexafffundvenue
Sean B. Rapai, Duncan McColl, R. Troy McMullin

Bibliographic record

VenueThe Forestry Chronicle · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsCanadian Museum of NatureMinistry of ForestsPositive Living NorthUniversity of Northern British Columbia
FundersFisheries and Oceans CanadaBC Hydro
KeywordsLichenCladoniaWoodland caribouHabitatWoodlandEcologyTransplantingGeographyEnvironmental scienceBiologyAgronomy

Abstract

fetched live from OpenAlex

The development of habitat restoration techniques for restoring critical woodland caribou (Rangifer tarandus caribou) winter habitat will play an important role in meeting the management thresholds in woodland caribou recovery plans. The goal is to restore disturbed environments within critical winter habitat for the declining woodland caribou. Woodland caribou are diet specialists, utilizing lichen-rich habitat for forage during winter months. Cladonia sub-genus Cladina is the most frequently eaten species during this time. Herein, we provide: 1) A review of previously used methods for transplanting Cladonia sub-genus Cladina and their feasibility in restoring woodland caribou winter habitat; 2) A stepby- step protocol on how to carry out a terrestrial lichen transplant program (using Cladonia sub-genus Cladina and C. uncialis); and, 3) An evaluation of our protocol through the establishment of a case study in northern British Columbia. Our results indicate that transplanting C. sub-genus Cladina fragments is the most efficient technique for transplanting terrestrial lichen communities, but transplanting lichen ‘patches’ or ‘mats’ may also be effective.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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 designBench or experimental
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
Published2017
Admission routes3
Has abstractyes

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