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Record W4411266373 · doi:10.1139/cjb-2024-0076

Impact of erect shrubs on the cover and fruit productivity of berry species in subarctic Canada

2025· article· en· W4411266373 on OpenAlexafffundvenueabout
Isabelle Lussier, Noémie Boulanger‐Lapointe, Stéphane Boudreau, Esther Lévesque

Bibliographic record

VenueBotany · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsUniversity of VictoriaUniversité du Québec à Trois-RivièresUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of CanadaArcticNet
KeywordsSubarctic climateBiologyBerryBotanyProductivityCover (algebra)Ecology

Abstract

fetched live from OpenAlex

Berry species are an important source of food in late summer for resident and migrant animals, and an integral part of the diet and culture of Indigenous Peoples. This study investigated how the presence of erect shrub patches (cover > 25%) affected the occurrence, cover, and fruit productivity of Vaccinium uliginosum L., Vaccinium vitis-idaea L., and Empetrum nigrum L. in the vicinity of Umiujaq, a subarctic community that has experienced a rapid increase in erect shrub cover since the 1990s. Our results indicated that berry species are ubiquitous in the area although the likelihood of occurrence is nearly three times lower under shrub patches. The cover and fruit productivity of V. uliginosum and E. nigrum diminished under erect shrub patches and this effect was more pronounced at the center of patches. In contrast, the cover and fruit productivity of V. vitis-idaea was not influenced by the presence of erect shrub patches. Finally, erect shrub patches delayed fruit ripening for V. uliginosum and V. vitis-idaea but differences could not be measured for E. nigrum. This study suggests that observed and forecasted increases in erect shrub cover in the Arctic may have widespread negative impacts on berry species.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.028
GPT teacher head0.265
Teacher spread0.237 · 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

Citations2
Published2025
Admission routes4
Has abstractyes

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