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Record W1728787300 · doi:10.1029/2012gb004349

Biological productivity along Line P in the subarctic northeast Pacific: In situ versus incubation‐based methods

2012· article· en· W1728787300 on OpenAlexaff
Karina E. Giesbrecht, Roberta C. Hamme, Steven Emerson

Bibliographic record

VenueGlobal Biogeochemical Cycles · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSubarctic climateProductivityIncubationPrimary productionEnvironmental scienceIn situMixed layerPrimary productivityNew productionOceanographyChemistryAnimal scienceAtmospheric sciencesNutrientBiologyPhytoplanktonEcologyGeologyEcosystem

Abstract

fetched live from OpenAlex

We compared net community production determined from an in situ O 2 /Ar mass balance (O 2 /Ar‐NCP) with incubation measurements of new and primary production in the subarctic northeast Pacific. In situ O 2 /Ar‐NCP was strongly correlated to new production from 24‐h 15 NO 3 − uptake integrated over the mixed layer ( 15 N‐NewP), if measurements were separated into high and low‐productivity conditions. Under high‐productivity conditions, O 2 /Ar‐NCP estimates were similar to 15 N‐NewP, whereas under low productivity conditions O 2 /Ar‐NCP was up to two times higher than 15 N‐NewP. The relationship between O 2 /Ar‐NCP and 24‐h 13 C primary production ( 13 C‐PP) was more variable, but with a consistent mean O 2 /Ar‐NCP: 13 C‐PP ratio of 0.52 ± 0.17 when only low‐productivity, summer measurements were considered. This relationship with primary production is perturbed by high productivity events such as a late‐summer, iron‐stimulated bloom observed at the offshore stations. Finally, we show that diapycnal mixing usually dominates the O 2 /Ar mass balance in winter in the subarctic Pacific, preventing the determination of NCP by the O 2 /Ar method at that time, except for one unusual stratification event in February 2007.

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)0.001
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 score0.034
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.284
Teacher spread0.247 · 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

Citations36
Published2012
Admission routes1
Has abstractyes

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