Bibliographic record
Abstract
Raymond J. Heuchling, a respected paper and pulp sales and marketing executive with 35 years of experience in the industry, is the Founder and President of The Heuchling Group, Inc., an organization dedicated to providing a broad range of consulting and trading services for growth-focused companies within the pulp and paper industry. Mr. Heuchling spent more than 30 years as an executive with New Brunswick, Canada-based Irving Forest Products. He played a major role in establishing the company’s first sales office in the United States. As Senior Vice President of Pulp and Paper Sales, he was responsible for Irving’s sales and marketing across the U.S., as well as the company’s business development activity. Dedicated to education and training to advance the industry, Mr. Heuchling has been an active member of the University of Maine Pulp and Paper Foundation for decades, serving on Public Relations Committee, the Executive Committee, and most recently as Chair of the Board. Mr. Heuchling served as a member of the Education and Training Committee of TAPPI, and a member of TAPPI’s Bio-Refining Committee. Mr. Heuchling is a past president of the Association of American Wood Pulp Importers, as well as a member of the Paper Industry International Hall of Fame. He is past president of the Paper Industry Management Association (PIMA), has served on the PIMA board of directors, and was chairman of the PIMA Foundation.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.288 | 0.154 |
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".