EVALUATION OF CANDIDATE FOR THE APPOINTMENT AS ASSOCIATE PROFESSOR ON TTS IN THE DEPARTMENT OF BIOCHEMISTRY & BIOTECHNOLOGY. The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Bibliographic record
Abstract
To Whom it may concern, \nDear The Islamia University of Bahawalpur, Bahawalpur, Pakistan \n \nAppointment of the candidate Dr. Muhammad Farooq as Associate Professor of Biochemistry & Biotechnology on TTS in the Department of Biochemistry & Biotechnology \n- According to the educational/professional qualification, the experience of the candidate Dr. Muhammad Farooq is evaluated as: excellent. \nThe candidate Dr. Muhammad Farooq graduated as a doctor of Veterinary medicine (DVM) at the University of Agriculture Faisalabad in Pakistan in 2001 and after that, he was Veterinary doctor in 2002-2004 in Saudi Arabia. He got the Ph.D. in 2010 at the NIBGE in Pakistan and he performed also different experience abroad such as at the University of Copenaghen, Danmark in which he was researcher assistant (2007-2008), Niigata University, Japan in which he was post-doc researcher (2010-2013) and again at the University of Copenaghen, Danmark (2013-untill now). The candidate has visited as researcher different laboratory, both national and international ones and he acquired important skills always keeping a good number of scientific publications. \n \n- According to the scientific production and bibliometrics, the experience of the candidate Dr. Muhammad Farooq is evaluated as: Very good \nIndividual contribution of the candidate to the research and development activities carried out: the candidate presents 38 publications, published with continuity in the last years; the 38 works presented by the candidate were published on International journals mainly in the first quartile and with an high average impact factor (average IF 3.81). Some publications are excellent and worthy of mention such as Nature neuroscience 2011 (IF 16.724), J Clin Invest 2010 (IF 12.575), Am J Hum Genet 2012 (IF 10.794). In the published papers, the individual contribution of the candidate is highlighted in the position of name (i.e. 13 first name). This indicates an active participation in the research carried out. \n \n \n \n- According to poster and oral presentations, the experience of the candidate Dr. Muhammad Farooq is evaluated as: Very good \nThe candidate presents 10 poster presentations and 4 oral ones; this is not an extremely high number but many of them are on international conference such as in Kyoto (Japan), Paris (France), Copenaghen (Danmark) , United Kingdom, Honolulu (Hawai), Nurnberg (Germany), Barcelona (Spain), Vancouver (Canada) and so on.. \n \nAccording also to other experience and parameters(scientific grants, practical laboratory experiences, attendance to sympoisia/conference/meeting etc...), the candidate Dr. Muhammad Farooq is evaluated as: Very good \n \n \n \n \nBased on the evaluation of the qualifications/titles and publications/research activities submitted by the candidate in the application and based on the entire application itself, I express my strong POSITIVE opinion on the evaluation of the candidate Dr. Muhammad Farooq as Associate Professor of Biochemistry & Biotechnology on TTS in the Department of Biochemistry & Biotechnology.
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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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.180 | 0.080 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".