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Record W2936420950 · doi:10.5539/mas.v3n2p83

Study on Cellulose-decomposed Actinomycetes in Soil in the Eastern of the Qinghai Plateau

2009· article· en· W2936420950 on OpenAlexvenueno aff
Yan Cai, Quanhong Xue, Zhanquan Chen, Rong Zhang

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCellulosePlateau (mathematics)Soil waterAgronomyChemistrySoil scienceMathematicsBiologyEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

The distribution of cellulose–decomposed actinomycetes was studied by conventional methods. Those microbes were separated in soil from the eastern part of Qinghai Plateau. The results indicated that: There were a certain amounts of cellulose-decomposed actinomycetes in soil from the eastern part of Qinghai Plateau. The number of cellulose-decomposed actinomycetes in vegetable field soil was higher than in grain field, but the strong cellulose-decomposed actinomycetes’ ratio of cellulose-decomposed actinomycetes in vegetable field soil was lower than that in grain field. The strong cellulose-decomposed actinomycetes’ ratio of the total cellulose-decomposed actinomycetes in natural soil had certain relevance with the soil organic matter content. In each kind of natural soil, the number of cellulose-decomposed actinomycetes in cumulated irrigated soil was the maximum, and the number of strong cellulose-decomposed actinomycete in castanozem was the most, and the strong cellulose-decomposed actinomycetes’ ratio of cellulose-decomposed actinomycete in marsh soil was the highest. The results may provide scientific basis for separation and screening of the cellulose–decomposed actinomycetes.

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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.235
Teacher spread0.212 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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