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Record W4287805210 · doi:10.5281/zenodo.5845538

D4.4 - Predictive Algorithms v2

2020· report· en· W4287805210 on OpenAlexaff
Ignacio Lacalle, Darío Alandes, Benjamín Molina, Sergio Vivó, Miguel Montesinos, Dejan Štepec, Tomaž Martinčič, Flavio Fuart, Damjan Murn, Matija Cankar, Gilda de Marco, Charles Garnier, Zhe Li, C. Périer, Erwan Simon, Grigoris Dimitriadis, Eirini Tserga, Dimitris Spyrou, Kontogiorgi Chryssanthi, Georgios Dioletis, Athanasios Chaldeakis, Thibault Guillon, Michel Le Van Kiem, Fabrice Klein

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typereport
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsCanadian AIDS Treatment Information Exchange
FundersEuropean Commission
KeywordsAlgorithmComputer science

Abstract

fetched live from OpenAlex

This deliverable document contains the development of new prediction and<br> forecasting algorithms in the ports where the need is greatest: to allocate energy<br> more efficiently and to forecast the infrastructure needs and trend of<br> environmental pollution in the ports. For different sectors within ports, there are<br> different requirements and thus various predictive models/algorithms must be<br> constructed to include different conditions. The proposed algorithms will be<br> applied using available data measurements to test their performance and<br> forecasting quality

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.006

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 head0.034
GPT teacher head0.243
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

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