Pulp and paper mill bacteria as a potential source of novel encapsulation materials / by Peter R. Godin.
Bibliographic record
Abstract
"A total of 194 bacteria were isolated from pulp and paper biofilms from a mill in Thunder Bay, Canada. Diversity of the 194 bacteria indicated that the amounts of bacteria found in these biofilms may be influenced by seasonal factors. From this stock of 194 bacteria, 55 isolates were preliminarily screened for their carbohydrate quantity and flocculation ability with hopes of identifying isolates that may be capable of producing an extracellular polymeric substance that could be used as a novel encapsulation material. Four isolates, 1, 2, 8, and 34 were selected from the carbohydrate screening and they were identified as a Flavobacterium sp. (isolate 1), Pseudomonas sp. (isolate 2), and a Sphingomonas sp. (isolate 34). Unfortunately, isolate 8 identity could not be confirmed using 16s rDNA sequencing and biochemical testing. Isolates 1 and 34 carbohydrate was harvested and lyophilized in attempts to create gels. Following gelation experiments using different concentrations of bacterial carbohydrate along with cation addition to the solution, it was observed that isolate 1 carbohydrate produced semi-solid gels at 2.0 and 1.0% w/v concentrations when FeC1 3 was added. Both isolate 1 and 34 carbohydrates appeared to enhance gelation of non-gelling concentrations of the known polysaccharides Gellan and Xanthan when mixed gel experiments were conducted. "--Abstract
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".