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Record W2966794670

Abundance and diversity of nematodes and microarthropods in established and newly converted agricultural soils in western Newfoundland and Labrador

2019· dissertation· en· W2966794670 on OpenAlexfundaboutno aff
Erika H. Young

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
FundersDepartment of Fisheries and Land Resources
KeywordsSoil waterAbundance (ecology)Soil biodiversitySoil biologyEcologyAgronomyManureSoil fertilityEnvironmental scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Nematodes and microarthropods are used as indicators of nutrient flow through complex soil food web interactions and thus are indicators of soil health. In this study, nematodes and microarthropods were extracted from natural and farmed land at two locations in Newfoundland and from biochar treated soil at one location in Labrador. Newfoundland soil had several combinations of crop and manure treatments. All soils were analysed for various abiotic soil fertility parameters. Farmed soil had a more stable and complex nematode community than adjacent natural soils. Manure application did not have an obvious impact on nematode composition but affected the microarthropod community. Biochar treatment resulted in changes to faunal composition and abundance though microarthropod populations were not well established in Labrador soils. Nematode and arthropod compositions were more strongly reflective of variations in soil pH than other measured parameters. All systems had bacterivore dominated nematode communities, an indication of bacterially driven soil metabolism.

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 score0.107
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.020
GPT teacher head0.236
Teacher spread0.216 · 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
Published2019
Admission routes2
Has abstractyes

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