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Record W4200392970 · doi:10.3390/w13243560

Alternative Use of Artificial Quarry Lakes as a Source of Thermal Energy for Greenhouses

2021· article· en· W4200392970 on OpenAlexaff
Jessica Chicco, Félix-Antoine Comeau, Alessandro Casasso, Cesare Comina, Nicoló Giordano, Giuseppe Mandrone, Jasmin Raymond

Bibliographic record

VenueWater · 2021
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEnvironmental scienceAquiferGroundwaterHydrology (agriculture)Greenhouse gasResource (disambiguation)Volume (thermodynamics)Thermal energyGeologyMining engineeringOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

In northern Italy, most greenhouses rely on gas or oil heaters which are sometimes subject to high operating costs. Several greenhouses are nearby quarry lakes, which are the legacy of the expansion of cities in the last decades, including Turin (NW Italy). About 20 quarry lakes were excavated close to the Po riverbed in the southern part of this urban area, along a belt of more than 30 km in length, with an overall volume exceeding 10 million m3 water. The study addresses these artificial lakes as a low enthalpy thermal energy source, potentially providing heat to surrounding agri-business buildings. Detailed temperature monitoring of a large lake quarry was conducted over two years at different depths, measuring the surrounding groundwater level as well. Two different behaviors of the lake during the winter and summer seasons enabled the definition of a quite low water mixing process between the surrounding aquifers and the lake (in the range of 2–4 °C). An evaluation of the heat extraction potential using the lake as a heat source, depending on water temperature and its volume, and a qualitative comparison with groundwater systems are proposed. This study contributes to increasing knowledge on an overlooked resource for sustainable heating.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.983

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)0.0000.000

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 head0.243
Teacher spread0.213 · 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 designBench or experimental
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

Citations10
Published2021
Admission routes1
Has abstractyes

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