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Record W4412222534

D7.6 – Demo site report #6 N. Denmark

2023· report· en· W4412222534 on OpenAlexaff
Martin Frandsen, Martin Veit, Michal Zbigniew Pomianowski, Kais Dai, Nikos Sofias, Dimitris Lokas, Ioannis Meintanis, François Veynandt, Pavlos Psimadas, Vagelis Alifragkis, Lorenzo Farina, Mattia Repossi, Rosaria Aversa, Giacomo Chiesa, Paolo Antonino Grasso, Florian Wenig, Christian Heschl, Giorgios Sokas, Athanasios Balomenos

Bibliographic record

VenueVBN Forskningsportal (Aalborg Universitet) · 2023
Typereport
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsSemtech (Canada)
Fundersnot available
KeywordsGeography
DOInot available

Abstract

fetched live from OpenAlex

Deliverable 7.6 (Demo site report #6 N. Denmark) is part of WP7 (Demonstration in operational environment). The report is public. The aim of T7.6 is to support the integration of the two NZEB residential single-family buildings into the PRELUDE platform and to integrate into these buildings, under real operation, the identified PRELUDE technologies. Data from the Danish demonstrations was also used for the development/validation of the PRELUDE technologies whenever possible. This reportsummarizes also the requirements and expectations of the Danish NZEB buildings for bothenergy use and IEQ. These are considered common for both buildings. The report summarizes and elaborates on the motivation to use the selected demonstration cases for the enablers developed by PRELUDE and defines the intervention plans and the rationale for implementing or not the individual solutions developed by PRELUDE . The task was to document the state of the buildings, their systems and the modifications that were carried out from the beginning of the project until M30. This process is carried out for each building and is documented in this report separately for each building case. The descriptions of buildings, systems, and modifications are documented in a detailed manner to provide a rich documentation of the technology enablers produced by PRELUDE. This includes securing the extensive monitoring of both buildings and the connection of the monitoring infrastructure to the PRELUDE platform (FusiX middleware) together with the identification of available data points in the middleware. Moreover, the first analysis of the data (energy and IEQ) was carried out to better identify the potential of the implementation of technology . Finally, the outcome of bilateral discussions and planning for each demo building and the PRELUDE technologies is summarized. Each technology that was identified as a potential for implementation and / or identified as being of value to the buildings or valuable for the technology to be tested in the operational environment is described.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.361
Threshold uncertainty score0.911

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3610.362

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 head0.065
GPT teacher head0.290
Teacher spread0.225 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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