MétaCan
Menu
Back to cohort

Common Activation for the Illusory-Rabbit and the Veridical-Rabbit versus Control Conditions Embedded in Probabilistic Cytoarchitectonical Maps of BAs 3a, 3b, 1, and 2

2015· other· en· W6922972500 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2015
Typeother
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsConjunction (astronomy)Probabilistic logicControl (management)Constant (computer programming)Plot (graphics)Contrast (vision)

Abstract

fetched live from OpenAlex

<p>Statistical T-maps (orange) obtained from the random-effects group analysis for the conjunction contrast of illusory-rabbit versus control data, and of veridical-rabbit versus the other (equivalent but independent) control dataset. The activation in right SI (peak at X = 36, Y = −32, Z = 66,<i>p</i> <0.005 for display purposes) is projected on the (A) sagittal, (B) coronal, and (C) transverse slices of the Montreal Neurological Institute standard brain, superimposed on gray-level-coded cytoarchitectonical probability maps (BA 3a in very light grey, BA 3b in light grey, BA 1 in grey, and BA 2 in deep grey), taken from Eickhoff et al. (2005). The plot (D) shows the parameter estimates for the experimental conditions (standard errors indicated in red), extracted from the peak of the activation (orange) in right SI. Note that both the veridical-rabbit and the illusory-rabbit conditions showed significantly higher activation than the two control datasets (see<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0040069#s4" target="_blank">Materials and Methods</a> for why the latter were split to allow conjunction analysis), which were equivalent, whereas the two rabbit conditions did not differ from each other.</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.277
Teacher spread0.243 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicEducational Robotics and EngineeringFrench-language works237,207