Thermal Monitoring of Natural Source Zone Depletion
Bibliographic record
Abstract
Abstract Natural depletion of subsurface petroleum liquids releases energy in the form of heat. The rate of natural source zone depletion (NSZD) can be derived from subsurface temperature data. An energy balance is performed to resolve NSZD‐generated energy in terms of W/m 2 . Biodegradation rates are resolved by dividing the NSZD energy by the heat of reaction in joules/mol. Required temperature data are collected using data loggers, wireless connections, and automated data storage and analysis. Continuous thermal resolution of monthly NSZD rates at a field site indicates that apparent monthly NSZD rates vary through time, ranging from 10,000 to 77,000 L/ha/year. Temporal variations in observed apparent NSZD rates are attributed to processes governing the conversion of CH 4 to CO 2 , as opposed to the actual rates of NSZD. Given a year or more of continuous NSZD rate data, it is anticipated that positive and negative biases in apparent NSZD rates will average out, and averaged apparent NSZD rates will converge to true NSZD rates. An 8.4% difference between average apparent NSZD rates over a 31‐month period using the thermal monitoring method and seven rounds of CO 2 efflux measurements using CO 2 traps supports the validity of both CO 2 trap and thermal monitoring methods. A promising aspect of thermal monitoring methods is that continuous data provide a rigorous approach to resolving the true mean NSZD rates as compared to temporally sparse CO 2 trap NSZD rate measurements. Overall, a vision is advanced of real‐time sensor‐based groundwater monitoring that can provide better data at lower costs and with greater safety, security, and sustainability.
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 | 0.001 | 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".