Building on Sub-Arctic Soil: Geopolymerization of Muskeg to a Densified Load-Bearing Composite
Bibliographic record
Abstract
The marshy water-saturated soil typical of the sub-Arctic represents a considerable impediment to the construction of roads, thereby greatly hindering human habitation and geological excavation. Muskeg, the native water-laden topsoil characteristic of the North American sub-Arctic, represents a particularly vexing challenge for road construction. Muskeg must either be entirely excavated, or for direct construction on muskeg, a mix of partial excavation and gradual compaction with the strategic placement of filling materials must be performed. Here, we demonstrate a novel and entirely reversible geopolymerization method for reinforcing muskeg with wood fibers derived from native vegetation with the addition of inorganic silicate precursors and without the addition of extraneous metal precursors. A continuous siloxane network is formed that links together the muskeg, wood fibers, and added silicates yielding a load-bearing and low-subsidence composite. The geopolymerization approach developed here, based on catalyzed formation of a siloxane network with further incorporation of cellulose, allows for an increase of density as well as compressive strength while reducing the compressibility of the composite.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".