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Record W4255934724 · doi:10.32920/ryerson.14656515

Earthworm Populations in Agricultural Green Roofs and their Influence on Soil Nitrogen, Greater Toronto Area

2021· preprint· en· W4255934724 on OpenAlexaffabout
Caitlin Victoria Santos

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsEarthwormSoil waterEnvironmental scienceAgronomySoil fertilityAgricultureNitrateNitrogen cycleNitrogenEcologyEnvironmental chemistryChemistryBiologySoil science

Abstract

fetched live from OpenAlex

Earthworm consumption and egestion of organic materials can increase bioavailable nitrogen in soils. Along with other benefits resulting from their burrowing activities, this process can increase soil fertility. This research investigated whether earthworms were present, and whether a relationship between earthworms and increased ammonium and nitrate levels was seen in the soils of the agricultural green roofs sampled in the greater Toronto area. Earthworms were found at several of the agricultural green roofs, but low soil moisture, low organic carbon, shallow depth, and compactness may have inhibited the establishment of earthworm populations in some soils. Results showed a statistically significant increase in levels of ammonium, but not in nitrate, with the increasing presence of earthworms. Findings indicate that some degree of increased bioavailable nitrogen benefits, resulting from earthworm presence, that are evident in conventional agricultural soils, can also be possible in agricultural green roofs, with attention to management of soil conditions that support earthworm populations.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.238
Threshold uncertainty score0.478

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.229
Teacher spread0.195 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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