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Record W1978031661 · doi:10.4319/lom.2003.1.10

Climate warming experiments: Design of a mesocosm heating system

2003· article· en· W1978031661 on OpenAlexaff
Helen M. Baulch, T. Nord, M. Y. Ackerman, J. D. Dale, Roderick R. O. Hazewinkel, D. W. Schindler, Rolf D. Vinebrooke

Bibliographic record

VenueLimnology and Oceanography Methods · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of ReginaUniversity of Alberta
FundersAmerican Society of Clinical Oncology
KeywordsMesocosmEnvironmental scienceHeat exchangerMeteorologyHydrology (agriculture)Atmospheric sciencesEcologyGeologyGeotechnical engineeringEngineeringEcosystem

Abstract

fetched live from OpenAlex

Research into the impacts of climate change on lakes requires novel experimental methods that enable realistic tests of the effects of increased water temperatures on communities. This article describes the design of a heating system that has been used in situ to study the effects of an increase in lake surface temperatures on littoral communities. Water within four 700‐L enclosures was heated using a propane‐fuelled heat exchange system. Hot water was circulated through a network of heat exchange pipes nested in the bottom of enclosures and temperature within the enclosures was controlled electronically by regulating water flow through a series of valves. The system performed well, with temperatures within warmed enclosures paralleling diurnal fluctuations within control enclosures. The average temperature difference between warm and control enclosures of 4.5°C was close to our target temperature difference of 5°C. Strengths of the experimental system are discussed and potential improvements, including improved heat retention and a design adjustment to facilitate repair in the event of lightning damage are noted. The system is adaptable to larger and smaller volumes, different temperature regimes, and can be adapted for use in pelagic systems.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.520

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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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