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Record W2170129680 · doi:10.3791/1159

Seawater Sampling and Collection

2009· article· en· W2170129680 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Visualized Experiments · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaKillam TrustsTula Foundation
KeywordsEnvironmental scienceSeawaterSampling (signal processing)Biomass (ecology)OceanographyGeologyComputer science

Abstract

fetched live from OpenAlex

This video documents methods for collecting coastal marine water samples and processing them for various downstream applications including biomass concentration. nucleic acid purification, cell abundance, nutrient and trace gas analyses. For today's demonstration samples were collected from the deck of the HMS John Strickland operating in Saanich Inlet. An A-frame derrick, with a multi-purpose winch and cable system, is used in combination with Niskin or Go-Flo water sampling bottles. A Conductivity, Temperature, and Depth (CTD) sensor is also be used to sample the underlying water mass. To minimize outgassing, trace gas samples are collected first. Then, nutrients, chemistry, and cell counts are determined. Finally, waters are collected for biomass filtration. The set-up and collection time for a single cast is approximately 1.5 hours at a maximum depth of 215 meters. Therefore, a total of 6 hours is generally needed to complete the four-part collection series described here.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.273

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 head0.062
GPT teacher head0.425
Teacher spread0.363 · 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