ICOTS-7, 2006: Ayuga Téllez, González García and Moral Medina STATISTICS EDUCATION APPLIED TO AGROFORESTRY AREA (2005)
Bibliographic record
Abstract
Applied Statistics may be regarded as a basic as well as technical subject in engineering, and therefore play a central role in the agro-forestry engineer professional curriculum. With the purpose of implementing a new learning method for statistics, within the framework of European Higher Education, an experimental evaluation was organised along a quarter in the academic year 2004/05 for students following the third grade at the Faculty of Forestry Engineering in the Madrid Polytechnic University. The teaching methodology goal was to improve students ’ oral information searching, use of technologies and analysis and synthesis capacities, while, at the same time providing them with a basic knowledge about the subject. GLOBAL EDUCATION OF ENGINEERS The technological evolution and the possibility of working in very distant locations support the idea of linking the practical training necessary to the work of engineers with a basic education which is wide enough to allow a long-range vision of professional activity. In this way, engineers will be able to adapt to new methods and techniques as well as to new work organisation. What really matters in higher education is not the contents the students learn during the
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.031 |
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".