Bibliographic record
Abstract
レンゴーでは，「軽薄炭少」を環境経営のキーワードとして，より軽く薄く，CO2排出量の少ないパッケージづくりに取り組んでいる。それは，段ボールの製品開発から始まり，各製造工程や輸送工程での省エネ推進に至るが，八潮工場でも板紙工場として提供できる新商品開発，マシン改造（設備投資），そして小集団による取り組みを通じて省エネに心掛けてきた。特に小集団活動では従来の手法に変え，コンサルタントを招へいして原理原則とエネルギー収支の検証，最新技術情報の収集と具体化，これらを推進する組織・人材の育成，といった柱を掲げ，各職場から若手中心のメンバーを選抜し実行力のある組織として省エネサークルを結成した。活動では単に立案だけでなく，継続的な省エネルギーを実施する「体制」及び「手順」の構築に力を入れ，PDCAサイクルによる継続的な活動を目指し，全員参加型の草の根活動による省エネ活動を実践した。その事例を紹介することで平成26年度省エネ大賞経済産業大臣賞を拝受したが，今回は受賞講演を基に八潮工場での取り組み内容を紹介する。
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".