RETRACTED: Identification and delineation of potash deposits in Saskatchewan, Canada using pulsed radar technology
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Editorial notice: This article has been retracted. See the associated retraction notice here . Saskatchewan, Canada, contains almost half of the world’s known potash deposits, within the Middle Devonian Prairie Evaporite Formation. As the global demand for potash increases, so does the need for faster, greener and cheaper methods of potash identification and extraction. The primary methods for exploration and development of potash include traditional 2D and 3D seismic methods, along with exploration drilling, downhole geophysical logging, coring, and assays for geological interpretation. One challenge with existing seismic geophysical methods in potash exploration is the difficulty in differentiating the responses and interpretation between the evaporite (sylvinite and halite (NaCl)) beds which are intermittently deposited within the Prairie Evaporite Formation. We field tested a new pulsed radar method for subsurface geophysical measurements at the Vanguard Area, Saskatchewan, to demonstrate whether subsurface evaporties could be directly identified non-invasively from ground level. In the Vanguard Area, the Prairie Evaporite Formation occurs at a depth of approximately 1500 m below ground surface (BGS). Achieving deep penetration of the transmitted pulsed radar wave packets, whilst discerning the materials from which the reflected wave packets (containing difference frequency and energy levels) bounced back from, was another scientific challenge. Following robust testing and due diligence on the method, we have been able to confirm that the pulsed radar method is capable of identifying broad lithological zones, differentiating halite units from sylvinite units (the potash-bearing members) within the Prairie Evaporite Formation, and also differentiating individual sub-members within each major potash member. A high-level identification of potash grade (%KCl) of the potash sub-members was also assessed. Based on this preliminary work, the results presented provide significant evidence and resulting confidence in the promise of utilizing the pulsed radar technology for successful identification and delineation of potash deposits
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.018 |
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".