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Relationships between soil chemistry, microbial biomass and the collembolan fauna of southern Québec sugar maple stands

2000· article· en· W1489818653 on OpenAlexafffundvenueabout
Madeleine Chagnon, David Paré, Christian Hébert

Bibliographic record

VenueEcoscience · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversité du Québec à Montréal
FundersCanadian Forest Service
KeywordsEpigealHumusBiomass (ecology)MapleSoil biologyCanonical correspondence analysisFaunaEcologyBotanyChemistryBiologySoil waterSpecies richness

Abstract

fetched live from OpenAlex

The relationships between the presence of endogeic and epigeic collembolan species and chemical and microbiological top soil parameters were examined for eight sugar maple forests. While the composition of the tree strata was similar among sites, the soil conditions varied widely and encompassed three regions of different geological origins as well as contrasting humus types. Endogeic species were extracted using Berlese-Tullgren equipment, whereas epigeic species were collected with pit-light traps (Luminoc®). In all, 92 species from 14 families and 36 genera were identified. The association between sampling locations and soil parameters was determined by principal component analysis (PCA), and the relationships between epigeic and endogeic collembolan communities and soil parameters were determined by canonical correspondence analysis (CCA and DCCA). The DCCA revealed that the distribution of endogeic species of the collembolan fauna was related to organic matter content, including pH, C, N, and C/N ratio. Sites were clustered according to humus types. Epigeic species were influenced by available P and exchangeable K and Mg. Sites were clustered according to geographical distribution, suggesting that for epigeic species, regional occurrence could be a stronger determinant of community composition than soil parameters.

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 score0.238
Threshold uncertainty score0.998

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 head0.014
GPT teacher head0.188
Teacher spread0.174 · 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

Citations28
Published2000
Admission routes4
Has abstractyes

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