Mobile Technologies for Geographic Response Plans (V2.0)
Bibliographic record
Abstract
Companies in the oil & gas industry must undertake extensive environmental studies for every new project. The case of the Trans Canada East Energy pipeline project illustrates this phenomenon. These studies include large-scale field surveys campaigns that generate a huge amount of raw geospatial data. A key component of this process resides in the analysis of this data in order to create knowledge. In this perspective, while many mobile geospatial collectors are available, we focused on designing a complete mobile GIS. Most field experts are not data management specialists and vice versa. Our goal is to use mobile technology to reduce the gap between field observations and geospatial data analysis. Bringing data analysis capabilities directly in the field is advantageous because it provides useful insights in real-time and it can avoid costly mistakes. We have also focused on enhancing the planning phase prior to the field surveys, such as allowing importation of external data into our system. We encountered many challenges while developing our mobile geospatial solution for geographic response plans. One critical element was the mean of transportation used during the surveys. For instance, airborne surveys using a helicopter requires specific navigational features to be accurate. Another element was the availability of internet connections. We had to permit offline map layer in order to provide high quality satellite imagery. Finally, power efficiency is critical for long field surveys. We had to optimize our mobile application in order to maximize the battery's lifespan while retaining enough features such as the GPS precision. Overall, the challenge was to design a system that would permit monitoring that could take place years after the initial data collection. This affected the way we had to implement data sharing, storage and export.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".