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Record W2138396770 · doi:10.1636/t07-51sc.1

Relevance of collected juveniles to the analysis of spider communities

2008· article· en· W2138396770 on OpenAlexaff
Tara E. Sackett, Christopher M. Buddle, Charles Vincent

Bibliographic record

VenueJournal of Arachnology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsAgriculture and Agri-Food CanadaMcGill University
Fundersnot available
KeywordsBiologySpiderSpecies richnessSpecies evennessEcologySampling (signal processing)HabitatZoology

Abstract

fetched live from OpenAlex

Spider field collections often consist of a high percentage of immature specimens that are not identifiable to species; in many studies these juveniles are discarded and not used in analyses. To evaluate if this practice affects the results of a community study, we sampled foliage-dwelling spiders in two habitats, reared the collected immature spiders until maturity, and identified them to species. We tested if measurements of species richness, evenness, and assemblage composition changed with the exclusion of data from immature specimens by analyzing two datasets: one including mature spiders only, the other including both mature and immature spiders (complete dataset). Nine of the total 49 spider species were collected only as juveniles, but only one of these nine species, Philodromus praelustris Keyserling 1880, was common (≥ 10% of collection). The distribution of individuals among species was more even in the complete dataset than the mature-only dataset, which could either indicate differences in composition or reflect sampling effort. However, species richness estimates were similar regardless of dataset, and there were only small changes in species composition of the samples between datasets, suggesting that there were not important compositional differences between the samples in each dataset. The inclusion of immature spiders in the data in this study yielded the same results that would occur with increased sampling effort.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.033
GPT teacher head0.257
Teacher spread0.224 · 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.

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

Citations14
Published2008
Admission routes1
Has abstractyes

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