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Record W2014995804 · doi:10.3791/1163

Small Volume (1-3l) Filtration of Coastal Seawater Samples

2009· article· en· W2014995804 on OpenAlexafffund
David A. Walsh, Elena Zaikova, Steven Hallam

Bibliographic record

VenueJournal of Visualized Experiments · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaKillam TrustsTula Foundation
KeywordsFiltration (mathematics)Volume (thermodynamics)Environmental scienceSeawaterBiomass (ecology)Pulp and paper industryChromatographySampling (signal processing)ChemistryEnvironmental chemistryEnvironmental engineeringFilter (signal processing)BiologyEcologyMathematics

Abstract

fetched live from OpenAlex

The workflow begins with the collection of coastal marine waters for downstream microbial community, nutrient and trace gas analyses. For today's demonstration samples were collected from the deck of the HMS John Strickland operating in Saanich Inlet. This video documents small volume (approximately 1 L) filtration of microbial biomass from the water column. The protocol is an extension of the large volume sampling protocol described earlier, with one major difference: here, there is no pre-filtration step, so all size classes of biomass are collected down to the 0.22 microm filter cut-off. Samples collected this way are ideal for nucleic acid analysis. The set-up, filtration, and clean-up steps each take about 20-30 minutes. If using two peristaltic pumps simultaneously, up to 8 samples may be filtered at the same time. To prevent biofilm formation between sampling trips, all filtration equipment must be rinsed with dilute HCl and deionized water and autoclaved immediately after use.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.997

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)0.0040.000

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 head0.043
GPT teacher head0.363
Teacher spread0.320 · 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.

Study designBench or experimental
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

Citations7
Published2009
Admission routes2
Has abstractyes

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