Chemical composition, particle size, and molecular weight distributions of chemically degraded guar gum solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Chemical degradation of guar gum solutions via the addition of a strong oxidant is a common process step in hydraulic fracturing. Unfortunately, this degradation step leads to the formation of an insoluble precipitate which clogs the porous rock formation, reducing efficiency, reducing oil recovery potential, and increasing energy costs. The chemical composition, particle size, and molecular weight distributions of the oxidatively degraded guar (“broken guar”) are largely unknown, making it difficult to develop mitigation strategies. In this work, broken guar gum solutions are systematically analyzed to understand the origin of the observed residue. Our results indicate that cellulose fibers and proteins, rather than galactomannan oligomers, are the two major components (>50%) of the solid residue (the water‐insoluble fraction of broken guar). This finding suggests that removal of the cellulose fiber and proteins from the guar source material may be a potential residue mitigation strategy. Separately, we provide evidence for a potential second mitigation strategy employing chemical additives to reduce aggregation of the insoluble species, effectively reducing their potential to cause formation damage.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it