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CollabSpace: Collaborative and Productive Solution for Corporates

2025· article· W7129307307 on OpenAlexaff
Vinayak Musale, Satish Kale, Prof. Akshada Dighe, Anant Kaulage, Amruta Aphale, Amruta Amune

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsTask (project management)VisibilityProductivityQuality (philosophy)Control (management)Core (optical fiber)Core competency

Abstract

fetched live from OpenAlex

In today's rapid corporate environment, managing tasks, builds, versions and QS processes is efficient to ensure productivity and smooth project execution. Traditional tools for task management, such as Jira, Asana, and Trello, are extensively fragmented and often do not provide robust integration between the core aspects of the development lifecycle, including build tracking, version control, and quality assurance. This increases overhead, reduces visibility and slows down the project's revolution. Collabspace is developed to remedy these flaws by integrating tasks, build management, version control and QA validation into a single coherent platform. In this article, the motivation behind Collabspace, a development method for platform design, is a comparative analysis with existing tools and the potential for future developments aimed at improving team collaboration, project management and productivity.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.525
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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