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Record W37628582 · doi:10.3390/genes14081529

Penerapan Teknik Imotilisasi Menggunakan Acepromazine dalam Transportasi Kering Ikan Nila (Oreochromis niloticus) dengan Pembedaan Jenis Kelamin

2012· article· en· W37628582 on OpenAlexfundno aff
Rio Septiadi S. S. M. Tampubolon

Bibliographic record

VenueGenes · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsnot available
FundersCanada Research Chairs
KeywordsOreochromisMedicineMathematicsBiologyFish <Actinopterygii>Fishery

Abstract

fetched live from OpenAlex

The deep terrestrial subsurface, hundreds of meters to kilometers below the surface, is characterized by oligotrophic conditions, dark and often anoxic settings, with fluctuating pH, salinity, and water availability. Despite this, microbial populations are detected and active, contributing to biogeochemical cycles over geological time. Because it is extremely difficult to access the deep biosphere, little is known about the identity and metabolisms of these communities, although they likely possess unknown pathways and might interfere with deep waste deposits. Therefore, we analyzed rock and groundwater microbial communities from deep, isolated brine aquifers in two regions dating back to the Ordovician and Devonian, using amplicon and whole genome sequencing. We observed significant differences in diversity and community structure between both regions, suggesting an impact of site age and composition. The deep hypersaline groundwater did not contain typical halophilic bacteria, and genomes suggested pathways involved in protein and hydrocarbon degradation, and carbon fixation. We identified mainly one strategy to cope with osmotic stress: compatible solute uptake and biosynthesis. Finally, we detected many bacteriophage families, potentially indicating that bacteria are infected. However, we also found auxiliary metabolic genes in the viral genomes, probably conferring an advantage to the infected hosts.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.649

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)0.0010.000

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 head0.023
GPT teacher head0.207
Teacher spread0.184 · 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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