Bibliographic record
Abstract
Mansour Mesdaghi was born in 1941 in Ghazvin, Iran, where he completed his elementary and high school.After graduation from Gorgan Forest and Range school, he had been employed in Ministry of Natural Resources as a technician in FAO branch of United Nation in collaboration with Dr. D. L. Goodwin, the Range Scientist at Houmand Range Research Station. He received his B.A degree in Economics at Tehran College of Business in 1973. Then, in 1975, he received a governmental fellowship to accomplish his M.Sc and Ph.D. in Range Ecology at Northern Arizona University and University of Wyoming. The results of his dissertation on vegetation of saline and alkaline habitats of Red Desert in Wyoming was published in Shrublands of Wyoming by his supervisor, Professor Herbert G. Fisser.After continuation his career as an expert in Research Institute and in Forest and Range Origination, he was transferred to the College of Natural Resources in Gogran where he had taught the courses of Range Measurement, Quantitative Plant Ecology, Phytosociology, Advanced Statistical Methods, and Range Research Techniques. During his sabbatical leaves at Colorado State University, 1993-1995, USA and at University of British Colombia, 2002-2003, Professor Mesdaghi integrated the idea of socio-economics in rangeland inventory and evaluation and presented his results in Proceedings of the V International Rangeland Congress in 1995, USA, and XX International Grassland Congress, in Canada, 2015.Professor Mesdaghi is the author of Range Management in Iran, Plant Ecology, and Statistical and Regression Methods. He has also translated the Range Management Books such as: Principles and Practices, Ecological Methodology, Vegetation Description and Analysis, Wildlife Ecology and Management, Walter’s Vegetation of Earth which were the best books of outstanding scientists in the world.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.231 | 0.119 |
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".