Bibliographic record
Abstract
It is with great pleasure and admiration that I congratulate the Japan Society of Polymer Processing (JSPP)for２ ０years of achievement.The Journal Seikei-Kaku ，the annual meetings and the interactions between its members helped enormously in transferring of knowledge and converting innovative research into improved and new processes and products.With its large membership base, and strong participation of both industries and universities the JSPP has made a momentous impact in advancing the frontiers of knowledge and technology in the processing of polymeric materials, not only in Japan, but also all around the world.The thematic range of topics, which have been presented and discussed during JSPP meetings or published in Seikei-Kaku, covers both fundamental work, such as rheological characterization, viscoelastic simulation, flow visualization and technological innovations such as in process machinery, automotive plastics, packaging films, fibers and much more.Globally, polymers are produced in huge quantities of more than２ ０ ０million tons annually and growing at twice the rate of growth of the global economy.The innovations advanced by JSPP in such processes as extrusion, injection molding, blow molding, thermoformimg, compounding and in materials such as polymer blends, reinforced plastics, nanocomposites and bioplastics have had very considerable global impact.I am also welt aware of the international cooperation that has been promoted col-
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.115 | 0.117 |
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".