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Record W35911590 · doi:10.3389/fnhum.2022.858378

Diseño y cálculo eléctrico de parque fotovoltaico de 2,1 MWn conectado a red

2014· article· en· W35911590 on OpenAlexfundaboutno aff
Javier Catalán Martínez

Bibliographic record

VenueFrontiers in Human Neuroscience · 2014
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Visual disturbances are amongst the most commonly reported symptoms after a traumatic brain injury (TBI) despite vision testing being uncommon at initial clinical evaluation. TBI patients consistently present a wide range of visual complaints, including photophobia, double vision, blurred vision, and loss of vision which can detrimentally affect reading abilities, postural balance, and mobility. In most cases, especially in rural areas, visual disturbances of TBI would have to be diagnosed and assessed by primary care physicians, who lack the specialized training of optometry. Given that TBI patients have a restricted set of visual concerns, an opportunity exists to develop a screening protocol for specialized evaluation by optometrists-one that a primary care physician could comfortably carry out and do so in a short time. Here, we designed a quick screening protocol that assesses the presence of core visual symptoms present post-TBI. The MOBIVIS (Montreal Brain Injury Vision Screening) protocol takes on average 5 min to perform and is composed of only "high-yield" tests that could be performed in the context of a primary care practice and questions most likely to reveal symptoms needing further vision care management. The composition of our proposed protocol and questionnaire are explained and discussed in light of existing protocols. Its potential impact and ability to shape a better collaboration and an integrative approach in the management of mild TBI (mTBI) patients is also discussed.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations0
Published2014
Admission routes2
Has abstractyes

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