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Record W2145997729 · doi:10.5194/gmd-8-2231-2015

EMPOWER-1.0: an Efficient Model of Planktonic ecOsystems WrittEn in R

2015· article· en· W2145997729 on OpenAlex
Thomas R. Anderson, Wendy C. Gentleman, Andrew Yool

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.

Bibliographic record

VenueGeoscientific model development · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsDalhousie University
FundersNatural Environment Research CouncilSight Research UK
KeywordsTestbedPlanktonComputer scienceEcosystem modelEcosystemSet (abstract data type)Simple (philosophy)Marine ecosystemModular designEcologyBiology

Abstract

fetched live from OpenAlex

Abstract. Modelling marine ecosystems requires insight and judgement when it comes to deciding upon appropriate model structure, equations and parameterisation. Many processes are relatively poorly understood and tough decisions must be made as to how to mathematically simplify the real world. Here, we present an efficient plankton modelling testbed, EMPOWER-1.0 (Efficient Model of Planktonic ecOsystems WrittEn in R), coded in the freely available language R. The testbed uses simple two-layer "slab" physics whereby a seasonally varying mixed layer which contains the planktonic marine ecosystem is positioned above a deep layer that contains only nutrient. As such, EMPOWER-1.0 provides a readily available and easy to use tool for evaluating model structure, formulations and parameterisation. The code is transparent and modular such that modifications and changes to model formulation are easily implemented allowing users to investigate and familiarise themselves with the inner workings of their models. It can be used either for preliminary model testing to set the stage for further work, e.g. coupling the ecosystem model to 1-D or 3-D physics, or for undertaking front line research in its own right. EMPOWER-1.0 also serves as an ideal teaching tool. In order to demonstrate the utility of EMPOWER-1.0, we implemented a simple nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model and carried out both a parameter tuning exercise and structural sensitivity analysis. Parameter tuning was demonstrated for four contrasting ocean sites, focusing on station BIOTRANS in the North Atlantic (47° N, 20° W), highlighting both the utility of undertaking a planned sensitivity analysis for this purpose, yet also the subjectivity which nevertheless surrounds the choice of which parameters to tune. Structural sensitivity tests were then performed comparing different equations for calculating daily depth-integrated photosynthesis, as well as mortality terms for both phytoplankton and zooplankton. Regarding the calculation of daily photosynthesis, for example, results indicated that the model was relatively insensitive to the choice of photosynthesis–irradiance curve, but markedly sensitive to the method of calculating light attenuation in the water column. The work highlights the utility of EMPOWER-1.0 as a means of comprehending, diagnosing and formulating equations for the dynamics of marine ecosystems.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.042
GPT teacher head0.220
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