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Record W6929525670 · doi:10.5063/4m92x7

Relative abundance patterns of terrestrial arthropods in Mojave National Preserve

2021· dataset· en· W6929525670 on OpenAlexaff

Bibliographic record

VenueUC Santa Barbara · 2021
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsYork University
Fundersnot available
KeywordsLarreaShrubTrophic levelAbundance (ecology)EcosystemFacilitationEcosystem engineerPollinatorInsect

Abstract

fetched live from OpenAlex

In dryland systems, shrubs often increase the productivity, abundance, and diversity of understory plants, however these positive interactions can also scale to other trophic levels. The facilitative effect of Larrea tridentata was hypothesized to extend beyond plants to the local insect community in the Mojave Desert. Pan traps were placed under shrubs and in open microsites to test the following predictions: 1) shrub-annual facilitation complexes increase insect abundance, richness, and diversity; and 2) specific insect families or functional groups respond to the shrub-annual facilitation complex more strongly due to increased resources. Abundance, richness, and diversity of the insect commu- nities associated with shrubs were significantly greater in shrub compared to open microsites. The families Sphecidae, Formicidae, Bradynobaenidae and Lauxaniidae were positively associated with shrubs while Scarabaeidae was associated with open microsites. However, there was no difference in the relative abundances of major functional groups, suggesting that the primary pollinators for this ecosystem are not sensitive to differences in floral resources at this scale. This study demonstrates that shrubs facilitate local insect communities and supports the hypothesis that plant-plant facilitation can extend to other trophic levels. Management of desert shrubs is thus an effective means to enhance many components of insect biodiversity.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.052
Threshold uncertainty score0.913

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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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