Managing user submissions User

Navigate to My Folder, select a folder, and click the folder name or next icon to view submitted files. Each submission shows detailed information including grammar, AI, and similarity scores.

Submissions table & bulk actions

Inside a folder, each submission displays: Name, Title, File, Language, Grammar Score, AI Score, Similarity, Paper ID, Submission Date, and Actions. Select multiple files for bulk operations.

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Priya Sharma User

Dashboard › My folder › NLP Research

Submissions (14)

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☐	Name ↑	Title ↑	File ↑	Lang.. ↑	Grammar BETA	AI Score	Similarity	Pap.. ↑	Submis.. ↑	Actions
☐	img conv..	report	📄 scan.pdf	English	NA	NA	⚠ 0%	4921035	12-03-26..	⇩ 🗑
☐	ml basics..	thesis	📄 updated..	English	72%	3%	7%	4918207	10-03-26..	⇩ 🗑
☐	cloud rpt..	paper	📄 cloud.docx	English	68%	89%	71%	4915890	08-03-26..	⇩ 🗑
☐	iot survey..	draft	📄 iot_v2.pdf	English	NA	0%	45%	4912441	05-03-26..	⇩ 🗑

Document Error: 1) Document is Corrupted/Encrypted 2) Document is in Image Format 3) Document does not have minimum 50 words 4) Document contains hidden characters

Bulk actions: Select multiple checkboxes to see Save to Repository, Bulk Report Download, and Delete options at the top.

Interactive guide

Submissions table & bulk actions

View all submission details. Select checkboxes for bulk Save, Download, or Delete operations.

Submission details

Name: The file name of the submitted document.
Title: The title associated with the submission.
File: Download icon to retrieve the original file.
Language: The language of the document.
Grammar Score: Reflects writing quality — higher scores indicate fewer errors.
AI Score: Flags sections that may have been created with AI tools.
Similarity: The percentage matching external sources. Click to open the analysis report.
Paper ID: Unique identifier for tracking submissions.
Submission Date: Exact date and time of upload.

Bulk actions

Select multiple submissions using checkboxes to access: Save to Repository, Bulk Report Download (ZIP), and Multiple Deletion. These options only appear when checkboxes are selected.

Analysis page — full walkthrough

Click a similarity percentage to open the analysis report. The toolbar provides features for sharing, downloading, saving, and configuring the report.

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Paper ID: 4821056 Author Name: Rohit K Submission Date: 2026-02-08 11:20:45

Title: Machine Learning Fundamentals

Machine Learning: Transforming Data into Knowledge

Introduction

Machine Learning (ML) is no longer just a theoretical concept studied in academic labs—it has become an integral part of modern technology. From voice assistants to recommendation engines, ML has quietly woven itself into the fabric of digital society. While many see ML as a subset of AI, it represents humanity's effort to build systems that can learn, adapt, and make predictions.

A Brief History of M...

Total Pages: 3Word Count: 2840

AI score 2% > Grammar 68% >

● Satisfactory (0-10%) ● Upgrade (11-40%) ● Poor (41-60%) ● Unacceptable (61-100%)

22%

Similarity

Matched sources (5)

Excluded sources (0)

☐ Primary SourceExclude

☐1

medium.com
Internet Data

☐2

springer.com
Publication

☐3

researchgate.net
Internet Data

☐4

ieee.org
Internet Data

☐5

acm.org
Internet Data

Interactive guide

Analysis page — full walkthrough

Toolbar features, score ranges, matched sources, download, share, and more options.

AI report

The AI score helps spot sections that may have been created with AI tools. Flagged sections are highlighted in cyan, with a percentage indicating the likelihood of AI-generated content.

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Paper ID: 4918207 Author Name: Aarti N Submission Date: 2026-02-10 09:15:30

Title: Data Structures Review

Modern data structures form the foundation of efficient software systems. From hash tables to balanced trees, each structure offers unique trade-offs between time and space complexity. Understanding these trade-offs is critical for building performant applications.

Arrays provide constant-time access but lack flexibility for insertions. Linked lists offer dynamic sizing but sacrifice random access speed. Graph structures enable modeling of complex relationships.

The choice of data structure depends on the specific use case. For real-time systems, priority queues and heaps ensure efficient scheduling. For search-heavy applications, binary search trees and tries provide logarithmic lookup times.

Advanced structures like B-trees power database indexing. Bloom filters enable probabilistic membership testing with minimal memory usage, making them ideal for large-scale distributed systems.

Similarity Score 8% >

38%

The DrillBit AI detection model identifies AI generated text from tools like ChatGPT.

It provides a percentage estimation of AI content and highlights specific sections.

DrillBit AI model serves as a preliminary indicator of AI generated text within a document.

Interactive guide

AI detection report

Cyan highlighted sections indicate AI-generated content. The percentage shows the estimated AI content ratio.

Grammar report

The Grammar Score reflects writing quality with four key metrics: Phrases Quality, Non-Duplicate Content, Indexed Content, and Grammar Info.

◈ DrillBit

Paper ID: 4918207 Author Name: Aarti N Submission Date: 2026-02-10 09:15:30

Title: Data Structures Review

Detailed Analysis

Submitted Text

Characters
5120

Words
842

Sentences
56

Lines
74

Reading and Execution Time

Reading
0 Hr, 3 Min

Speaking
0 Hr, 6 Min

Execution
0 Hr, 1 Min

1. Phrases Quality

Only Alphabets
83.20%

Only Numbers
00.15%

Alpha-numeric
00.48%

Special Chars.
16.17%

Unique Words 61.45%

Rare Words 38.92%

Common Words 48.70%

Word Length 05.83
Measures average word length (Characters per word).

Sentence Length 14.25
Measures average sentence length (words per sentence).

2. Non-Duplicate Content

3. Indexed content

Sl. No	Index	Lines	Words	% in Report
1	Other Data	74	842	100.0%	view

Submitted Text:

Line 1| Data Structures: A Foundation for Efficient Computing
Line 2| Introduction Modern software systems rely heavily on well-designed
Line 3| data structures to manage, organize, and retrieve information
Line 4| efficiently. From simple arrays to complex graph algorithms,
Line 5| understanding data structures is essential for building scalable
Line 6| applications that perform well under varying workloads.

Similarity Score 8% >

74%

Grammar

1. Phrases Quality:79%

2. Non-Duplicate Content:100%

3. Indexed Content:12%

4. Grammar Info:97%

Phrases Quality: Measures language effectiveness benchmarked against academic standards.

Non-Duplicate Content: Evaluates originality by identifying repetitive content.

Indexed Content: Measures structural quality and inclusion of essential sections.

Grammar Info: Evaluates grammatical accuracy including spelling, punctuation, and tense.

Interactive guide

Grammar report & detailed analysis

View Phrases Quality, Non-Duplicate Content, Indexed Content, and Grammar Info metrics.

Similarity score ranges

Satisfactory (0-10%): Minimal similarity, generally acceptable.
Upgrade (11-40%): Moderate similarity, needs review.
Poor (41-60%): High similarity, should be addressed.
Unacceptable (61-100%): Critical need for revisions.

Report downloads

Four report types are available from the Actions column: Similarity Report, Summary Report, Grammar Report, and AI Report.

Doc Error: If a file shows ⚠ 0% in the Similarity column, it indicates a document error. See the Doc Errors guide for details.

Related guides

File upload → Folder management → Doc errors →

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