MétaCan
Menu
Back to cohort
Record W2208076480 · doi:10.1109/pvsc.2015.7355665

Predicting the long term power loss from cell cracks in PV modules

2015· article· en· W2208076480 on OpenAlex
Vivek Gade, Narendra Shiradkar, Marco Paggi, Jared Opalewski

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsnot available
FundersInstitute of Gender and Health
KeywordsLaminationMaterials sciencePower lossFlash (photography)Characterization (materials science)Power (physics)Service lifeTerm (time)Power moduleStructural engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Long term power loss due to cell cracks can become a significant PV wear out mechanism. An experimental framework to assist in predicting the power loss from cell cracks during module service life is presented. The paper is primarily structured around cell crack origin in laminated modules, crack orientation, oriented crack reproduction and climatic testing of custom mini-modules. The results are experimentally verified by a series of accelerated tests involving mechanical load and humidity freeze tests. Periodic characterization using EL and flash I-V are used to study evolution of crack types/categories and performance loss due to cell cracks. Manufacturing modules cell crack data was collected from a population of a week of data post lamination. The data analyzed is for a certain batch of polycrystalline cell modules. Jabil has produced few GW of modules over the past years.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.996

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 head0.017
GPT teacher head0.241
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