Beyond environmental monitoring: Are automatic time-lapse cameras efficient tools for temperature measurement in remote regions?
Bibliographic record
Abstract
Automatic time-lapse cameras are frequently used to monitor snow height as well as snow and ice related processes occurring on slopes in cold regions because of the many advantages they bring to researchers. In addition to providing important visual information’s about the dynamic of the studied area, most of these types of cameras are now equipped with thermal sensors able to register temperature data for every picture taken. The instrumentation set up within Tasiapik Valley, near Umiujaq, in Nunavik (northern Québec), enabled us to assess the potential of automatic time-lapse cameras for temperature measurement by comparing data retrieved on photographs from time-lapse cameras with data from two nearby weather stations. Our results indicate that the temperature measurements from the time-lapse cameras from August to the onset of February are relatively accurate while their weaker performances for temperature measurement occurred in late winter and spring (March - June). Moreover, regardless of the year, time-lapse cameras were most accurate in the morning (09:00 AM – 11:00 AM), while in the afternoon (12:00 PM – 3:00 PM), they tended to over-estimate temperatures. Based on our observations and data analyses, this over-estimation of temperatures seems to be caused by external factors such as sky conditions and high values of downwelling shortwave radiation lasting from February to June at our study site. The local environment surrounding the cameras might also affect the performances of time-lapse cameras at temperature measurement.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".