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Record W1566369647 · doi:10.22621/cfn.v127i2.1441

The response of invertebrate populations in three undisturbed soils in southwestern Ontario, Canada, to variations in local soil properties, seasonal changes, and climate

2013· article· en· W1566369647 on OpenAlexfundvenueaboutno aff
I. W. E. Harris

Bibliographic record

VenueThe Canadian Field-Naturalist · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsnot available
FundersAgriculture and Agri-Food Canada
KeywordsAbundance (ecology)InvertebrateSoil waterEcologyAcariSeasonalityTaxonBiologyEnvironmental science

Abstract

fetched live from OpenAlex

Three distinctly different undisturbed mature forested sites at the northern limits of the Carolinian forest system in Lambton County, Ontario, were examined to test the hypothesis that the abundance of each order of soil invertebrates captured is dependent on a unique set of soil properties, seasonal changes, and climate variations. Sixteen independent variables were recorded over five consecutive years. With the exception of rainfall, air temperature, and soil temperature, means of the measured variables differed significantly (P < 0.05) among soils. Twenty-eight taxa of invertebrates were captured, of which Acari, Collembola, and Nematoda were most abundant. Only the mean of total abundance and the mean abundance of Acari, Nematoda, and Haplotaxida differed significantly (P < 0.05) among soils. Haplotaxida was the only taxon in all three soils found to be influenced significantly (P < 0.05) by seasonal variation. The usual mid-summer minimum in the abundance of Haplotaxida was latest and most clearly defined in the clay soil and earliest and least clearly defined in the sand soil. Regression analysis showed that each site is sufficiently separated in the factor space observed that the abundance of each invertebrate taxon is dependent on different combinations of local variables. The hypothesis was rejected.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.237

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 head0.035
GPT teacher head0.208
Teacher spread0.173 · 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

Citations1
Published2013
Admission routes3
Has abstractyes

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