Geophysical Induced Polarisation (IP) laboratory measurements on E. coli-sand mixtures
Bibliographic record
Abstract
The aim of the MIRACHL project is the characterisation and monitoring of in-situ remediation of chlorinated hydrocarbon contamination using an interdisciplinary approach and geophysical methods, such as DCIP (direct current induced polarisation) to investigate the remediation process. To interpret these geophysical field IP data, lab investigations with different kinds of bacteria are necessary to assess the sensitivity of the methods for these specific applications. A first experiment was conducted with E. coli bacteria. Bacteria were grown together with a rich source of nutrients (Luria-Bertani broth - LB) and mixed in different flasks with sterilised Ottawa sand. These bacteria-sand-mixtures were continuously shaken (30°C, 80 RPM) until defined endpoints (within 21 days) when the mixtures were harvested and packed in a 4-point sample holder to measure SIP (spectral induced polarisation), TDIP (time-domain induced polarisation) and SP (self-potential) under laboratory conditions. The same procedure was repeated with only the media-sand mixture to exclude any influences from just the nutrient and with water-sand mixtures. The results show a slightly increase in phase and a decrease in resistivity after several days with a decrease in phase that appears to coincide with die-off of the bacteria. Resistivity in general was very low (between 3-10 Ωm) due to the highly conductive LB-media containing 5 g/L NaCl. As a result, the phase effects are very small too. The positive phase which could be observed for the E. coli measurements was surprising and is not yet understood. As expected, the water-sand mixtures showed almost no phase shift and slightly higher resistivity values. The influence of the LB-media (nutrients) is very small and results only in a slightly lower resistivity than the E. coli-sand mixtures but in a higher resistivity than the water-sand samples. The self-potential measurements show no clear tendency, but this is most likely due to limitations in the sample holder. The TDIP data needs to be further processed but the resistivity values are in accordance with the SIP results. Scanning electron microscope (SEM) images showed E. coli bacteria attached to the sand grains and this could modify the grain surface (e.g. increasing the grain surface area and/or form a biofilm) and impact geophysical measurements. In the future, to support these observations with quantitative comparisons, the number of bacteria present in the sand will be determined using quantitative polymerase chain reaction (qPCR) to detect bacterial DNA (deoxyribonucleic acid).
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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 | 0.000 | 0.001 |
| 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".