Deposition data from: Assessing changes in indicators of fish health measured between 1997 and 2019 relative to multiple natural and anthropogenic stressors in Canada’s oil sands region using spatio-temporal modeling
Bibliographic record
Abstract
Oil sands facilities in northern Alberta influence the environment in many ways. Some, such as land clearance and mine construction have clear physical effects while others are also associated with changes in chemical indicators, such as stack emissions or wind-blown dust. Less well established are the biological effects of oil sands industrial activity. The purpose of the study using the data provided here (10.3389/fenvs.2024.1405357) was to examine the potential effects of various sources at oil sands sites emitting particles to the atmosphere and deposited on the landscape. The focus of the original study was not specifically on evaluating patterns of deposition, although some were alluded to in the results and discussion of that paper (and are presented as figures in the main manuscript or the supplemental information). More details about the methods are available elsewhere (10.3389/fenvs.2024.1405357), but these data were generated using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) atmospheric transport and dispersion model made available by the Atmospheric Research Laboratory. The datafiles provided here include the mass of particles deposited to the landscape for each respective year of study (1997, 1999-2001, 2004-2007, 2009-2019) for the months of June, July, and August; summer was chosen for its relevance to estimates of fish health, but also to limit the computation time. The final deposition location of particles emitted from all sources, including those adjusted post-hoc (and described in the original paper) are provided as ‘new_lat’ and ‘new_long.’ The unzipped file size is roughly 5GB. These data should be interpreted as spatially and temporally relative; their purpose was not to exactly reproduce absolute deposition, but instead to capture spatial and temporal variability in deposition patterns.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".