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Record W2922081097 · doi:10.5194/bg-2019-56

Technical Note: Low Cost Mesocosms Design for Studies of Tropical Marine Environments

2019· article· en· W2922081097 on OpenAlexfundno aff
R. Raygosa-Barahona, S. Putzeys, Jorge Herrera, Daniel Pech

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y TecnologíaUniversité du Québec à Rimouski
KeywordsMesocosmEnvironmental scienceEcosystemEnclosureForcing (mathematics)Tropical cycloneSeawaterOceanographyAtmospheric sciencesEcologyClimatologyComputer scienceBiologyGeology

Abstract

fetched live from OpenAlex

Abstract. Mesocosms are an alternative to in situ ocean environmental studies which are very difficult to implement due to the challenges that the aquatic environment impose. The design of a mesocosm should consider as many variables as possible of the ecosystem to be studied, in order to obtain results that are similar to those that would be obtained in the environment. The effects of tropical climatic conditions on a mesocosm enclosure were studied in order to evaluate their possible influence on the biological community. The mesocosm was equipped with an electric marine thruster as a means of avoiding stratification in the water contained in it. Also, the system is submerged in water to increase the thermal inertia and maintain the temperature variations within reasonable ranges. The design does not include auxiliary forcing cooling systems. The results revealed the influence of climatology on the mesocosms’ temperature and showed the feasibility of the proposed design in tropical environments. With high variations of ambient temperature (> 20 ºC, during the day), the variations in the mesocosm temperature were only 3 ºC. The range of temperature variations were similar to those that occur in certain tropical environments.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.999

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)0.0010.000

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 head0.030
GPT teacher head0.266
Teacher spread0.236 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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