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Record W2163323074 · doi:10.5194/bg-9-5353-2012

Nutritive and photosynthetic ecology of subsurface chlorophyll maxima in Canadian Arctic waters

2012· article· en· W2163323074 on OpenAlexafffundabout
Jean‐Louis Martin, Jean‐Éric Tremblay, Neil M. Price

Bibliographic record

VenueBiogeosciences · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesArcticNet
KeywordsEnvironmental scienceArcticIrradianceChlorophyll aPhotosynthesisEcosystemPrimary productionProductivityWater columnNitratePrimary productivityEcologyOceanographyChlorophyllAmmoniumNitrogenAtmospheric sciencesBiologyBotanyChemistryGeology

Abstract

fetched live from OpenAlex

Abstract. Assessments of carbon and nitrogen (N) assimilation in Canadian Arctic waters confirmed the large contribution of subsurface chlorophyll maxima (SCM) to total water-column production from spring to late fall. Although SCM communities showed acclimation to low irradiance and greater nitrate (NO3−) availability, their productivity was generally constrained by light and temperature. During spring–early summer, most of the primary production at the SCM was sustained by NO3−, with an average f-ratio (i.e., relative contribution of NO3− uptake to total N uptake) of 0.74 ± 0.26. The seasonal decrease in NO3− availability and irradiance, coupled to the build up of ammonium (NH4+), favoured a transition toward a predominantly regenerative system (f-ratio = 0.37 ± 0.20) during late summer and fall. Results emphasize the need to adequately consider SCM when estimating primary production and to revisit ecosystem model parameters in highly stratified Arctic waters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.187
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 source (direct Gemma or distilled Codex), 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

Citations59
Published2012
Admission routes3
Has abstractyes

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