Deriving the Metal and Alloy Networks of Modern Technology
Bibliographic record
Abstract
Metals have strongly contributed to the development of the human society. Today, large amounts of and various metals are utilized in a wide variety of products. Metals are rarely used individually but mostly together with other metals in the form of alloys and/or other combinational uses. This study reveals the intersectoral flows of metals by means of input-output (IO) based material flow analysis (MFA). Using the 2007 United States IO table, we calculate the flows of eight metals (i.e., manganese, chromium, nickel, molybdenum, niobium, vanadium, tungsten, and cobalt) and simultaneously visualize them as a network. We quantify the interrelationship of metals by means of flow path sharing. Furthermore, by looking at the flows of alloys into metal networks, the networks of the major metals iron, aluminum, and copper together with those of the eight alloying metals can be categorized into alloyed-, nonalloyed-(i.e., individual), and both mixed. The result shows that most metals are used primarily in alloy form and that functional recycling thereby requires identification, separation, and alloy-specific reprocessing if the physical properties of the alloys are to be retained for subsequent use. The quantified interrelation of metals helps us consider better metal uses and develop a sustainable cycle of metals.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".