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Record W3189507770 · doi:10.1111/geb.13370

The ghost of a giant – Six hypotheses for how an extinct megaherbivore structured kelp forests across the North Pacific Rim

2021· article· en· W3189507770 on OpenAlexafffund
Cameron D. Bullen, Alberto Alves Campos, Edward J. Gregr, Iain McKechnie, Kai M. A. Chan

Bibliographic record

VenueGlobal Ecology and Biogeography · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsTula FoundationUniversity of VictoriaSciencetech (Canada)Bamfield Marine Sciences CentreUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaHakai Institute
KeywordsKelp forestKelpMegafaunaEcologyEcosystemGeographyExtinction (optical mineralogy)Marine ecosystemBiologyFishery

Abstract

fetched live from OpenAlex

Abstract Aim The global decline of megafauna is believed to have had significant and widespread ecological impacts. One such extinction of likely important consequence is the 18th century extinction of the Steller’s sea cow (Hydrodamalis gigas); however, little has been written about how the loss of this megaherbivore may have impacted coastal ecosystem dynamics. Drawing on historical evidence, sea cow biology, kelp forest ecology, and the ecology of extant sirenians, we propose several discrete hypotheses about the effects Steller’s sea cows may have had on kelp forest dynamics of the North Pacific. Location North Pacific Ocean. Time period Pre‐1760s. Major taxa studied Steller’s sea cow (Hydrodamalis gigas). Results & conclusions The evidence we review suggests that Steller’s sea cows exerted substantial direct and indirect influences on kelp forests, likely affecting the physical ecosystem structure, productivity, nutrient cycling, species interactions, and export of nutrients to surrounding ecosystems. This suggests that kelp forest dynamics and resilience were already significantly altered prior to the influence of more recent and well‐known stressors, such as industrial fishing and climate change, and illustrates the important ecological roles that are lost with megafaunal extinction.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 designTheoretical or conceptual
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

Citations13
Published2021
Admission routes2
Has abstractyes

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