← Back to cohort
IC2PCT 2024 TOC
Arunendra Tripathi, Kavita Saini, Smita Mishra, Parth Belwal, Ashutosh Upadhyay, Anuj Dixit, Anuj Kumar, Mukta Jukaria, Nidhi Singh, Animesh Rawat, Navin Srivastava, Sachin Sharma, Shuchi Bhadula, Amit Juyal, Bansidhar Joshi, Bineet Kumar, Sanjeev Kumar, Yajnaseni Dash, Prateek Yadav, Ankit Bhatt, Nitin Thapliyal, Manisha Aeri, Abhishek Satyarthi, Vinay Kukreja, Rishabh Sharma, Deepak Upadhyay, Manika Manwal, Ajay Pratap, Divakar Yadav, Singha Pritha, Vinay Roy, N. Kukreja, S Chandran, Ankur Choudhary, Kuldeep Singh Kaswan, Singh Dhatterwal, Rishika Yadav, Kiran Malik, Aanjey Mani Tripathi, Priya Sharma, Shweta Dwivedi, Nitesh Singh Bhati, Santosh Kumar, Vivek Ghabak, Rakesh Chaugule, Rohit Singh, Kumar Saxena, Arun Jhapat, Ritu Shrivastava, Rajiv K. Srivastava, V. S. Madhumala, Kumar Tripathi, Priya Rana, Nitin Sharma, Nagendar Yamsani, Md Ozaif, Suhel Mustajab, Mahfooz Alam, Sunita Bhati, A. K. Bhati, Bharti Shah, Divya Pachauri, N.Noor Alleema, Mayank Patel, Dimple Tiwari, Anand Kumar, A. U. Akash, Kamakshi Agarwal, Akshat Sharma, Nitesh Singh, Mala Saraswat, A. K. Dubey, Garima Jaiswal, Ronson Danthy, Pratham Pai, Vidya Rao, Nikhitha Vadlamudi, Baby Naina, Tara Korivi, Kranthi Kumar Jandrasupalli, Lohith Kommavarapu, Thanmayee Mangu, Suraj Preetham, Ball Mukund, M. Tripathi, Drishti Arora, Farhan Asif, Amanpreet Kaur, Himanshu Jindal, Piyush Kumar, Laraib Hanif, Srinivas Lankalapalli, Akash Kapoor, Divyanshi Bhatia, Anubhav Kumar, Shikha Gupta, Rakesh Ramakrishnan, Balakumar Muniandi, Haritha Yennapusa, Nasmin Jiwani, T. Kiruthiga, Ketan Gupta, J Logeshwaran, Aanjey Tripathi, Saumya Shukla, Neha Chaudhary, B Teja, R Bhuvaneswari, Senduru Srinivasulu, G Unnathi, Saiful Islam, Rajdeep Bhattacharjee, Priyanath Das, Manoj Deepak, Upadhyay Kumar, Sanjay Ajani, Deep Vaghasiya, Divya Kapadia, Dhruvrajsinh Chauhan, Megh Thakkar, Raju Sanmugam, Archana Singh, Kamaldeep Bansal, Salil Jain, Devansh Mehra, Balasubramanian Prabhu, Vishal Jayaswal, Sharma Ji, Ashish Kumar, Varun Kumar, Ayushi Prakash, Aditya Singh, Aman Mishra, Mohammad Waseem, Shafiqul Abidin, Pratyush Chauhan, Rahul Sahani, Soham Datta, Ali Qadir, Manish Raj, Mohd. Mohsin, Densenet-Lstm Model, V Narayana, S Bhargavi, D Srilakshmi, V Sivaannapurna, D Akhila, Daniel Zinyongo, Sandeep Kumar, Mayank Kumar Goyal, Shakti Sharma, Smanish Raj, Mohsin Ali, Karun Sharma, Abhinendra Singh, Samridh Singh, Anusha Rajan, L. N. Mohapatra, Janardhana Addanki, Kasi Rao Mediga, Guttula Raja Babu, Parameswara Gottala, Ashwani Kumar, Sachin K. Patil, Yadav Kasula, Srikanth Kolluri, Akash Rajak, Rabi Panda, Amit Kumar, Yadav Balaram, Srikanth Kasula, Sachin Kolluri, N.N. Patil, T Jiwani, Maryam Mohamed, Al Hassan, Forkan Fahim, Mohamed Rukia, Usman Suleyman, Ajay Mansur, Payal Bansal, Devendra Somwanshi, Hiteshkumar Solanki, Shashikant Patil, M. Prasad, K. V. Lakshmi, Rao Pbv, V Prasanthi, Kiran Sree Pokkuluri, V Sree, G Rajesh Babu, Apoorv Varshney, Devendra Arya, Arpit Katiyar, Anil Kumar Dubey, Vijaya Choudhary, Paramita Guha, Shahadat Hussain, Alisha Banga, Sudhriti Sengupta, Shikha Yadav, J. Jean Justus, Tripti Tripathi, Rakesh Kumar, Palak Gupta, Mini Verma, Aarush Aggarwal, Sarthak Sharma, Priyodarshi Ghosh, V. Kamala Devi, Udaya Bhasker Manthati, R. Ramyadevi, G Sasidharan, Rohit Rastogi, Tushar Anand, Shubham Sharma, Sarthak Panwar, Bhumika Arora, Ruma Ghosh, Sumitrajit Dhar, Saptarshi Ghosh, Abhik Mandal, Arindam Ghosh, Partha Sarathi, Sujoy Saha, Pallavi Jain, S. Paul Singh, Chaitanya Kumar Saxena, Anitha Julian, Pavithra Bellare, Neha Shukla, Ayush Siloiya, Apoorva Singh, Aayushi Saini, Anirudh Sharma, Shinora Khan, Rithik Chhabra, Rajendra Kumarhari, Avikalp Goel, Raihan Karim, Ujjwal Singh, Rajendra Kumar, V Priyalakshmi, Rama Devi, Kadambri Agarwal, Aditi Mittal, Anushka Gupta, Sandhya Awsathi, Aum Patel, Simran Shrivastava, G. Sujatha, K Apoorva, S. Sangeetha
Bibliographic record
Venuenot available
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsFederated Co-operatives (Canada)
Fundersnot available
KeywordsComputer science
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.093
Threshold uncertainty score0.133
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.907 | 0.811 |
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 head0.023
GPT teacher head0.358
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
Study designNot applicable
Domainnot available
GenreOther
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
Published2024
Admission routes1
Has abstractno