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Nanosatellite nonlinear attitude control testing on an air bearing system

2012· article· en· W31901807 on OpenAlexaff
Junquan Li, Mark Post, Krishna Dev Kumar

Bibliographic record

VenueEcotoxicology and Environmental Safety · 2012
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsToronto Metropolitan University
FundersGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Attitude controlNonlinear systemFeed forwardRobustness (evolution)Lyapunov functionNonlinear controlLyapunov stabilityEngineeringControl engineeringSpacecraftControl systemReaction wheelAir bearingController (irrigation)Computer scienceControl (management)Aerospace engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Diets are shown to be capable of shaping the gut microbiota of earthworm, while the effects of distinct foods on bacterial communities of different digestive tracts of earthworm are unknown. For this purpose, cow dung (CD) and domestic sludge (DS) were chosen as diets for earthworms (Eisenia fetida), and different gut contents, namely gizzard + foregut area, hindgut, and mature vermi-compost were sampled for Illumina sequencing analysis. We found that there existed significant reductions in bacterial diversity and abundance in the gizzard + foregut area, where there were stable bacteria with the ability of biodegradation of xenobiotics, such as Amycolatopsis, Methylobacterium, Ralstonia, Ochrobactrum, and Sphingomonas. The decreases could be recovered in the hindgut and mature vermi-compost to different extents, suggesting that a bottleneck effect on the bacterial community occurred in the gizzard + foregut area. Beta-Proteobacteria was the most abundant subclass regardless of the different diets, and bacteria affiliated with gamma-, delta- and epsilon-subclasses were taken as food by the earthworms. Vermi-composts based on the various diets should be used differently according to different aims.

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 score0.241
Threshold uncertainty score0.916

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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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