Mitigation Banks and Other Conservation Lands at Risk from Preferential Groundwater Flow and Hydroperiod Alterations by Existing and Proposed Northeast Florida Mines
Bibliographic record
Abstract
When evaluating applications for proposed projects, agencies in the United States (U.S.) must consider mitigation options for the destruction of the aquatic environment such as wetlands, if no practicable alternative to the proposed action exists. Because attempts to create artificial wetlands as replacements for natural wetlands that were destroyed have had little success, particularly in the case of forested wetlands, mitigation options often include mitigation banks and conservation restrictions as permit requirements. In Florida, agencies issuing permits for projects involving groundwater alterations such as withdrawals from wells and mining, do not require an analysis of how on-site and off-site wetlands designated as mitigation, conservation or preservation for destroyed wetlands would be affected by fractures and other karst features that occur throughout the Floridan aquifer system (FAS). This study analyzed the underlying geological conditions in the FAS using lineaments as surface expressions of those conditions, in addition to indicators of preferential groundwater flow in an environmentally sensitive karst area of northeast Florida, including Baker and Clay Counties, where extensive mining has occurred in the recent past and more extensive mining is proposed in the immediate future. Established methods, such as spatial matching of scanned maps and remotely sensed images and control-point identification, were used for georectification of the analog maps. These techniques were applied to surface expressions of underlying fractures that were mapped in the early 1950s and 1970s, prior to extensive groundwater extractions, urbanization and mining in the region. The vegetation changes and reduced groundwater discharges from those alterations reduce the ability to identify fractures from post-development images. Geospatial analyses of lineament distribution revealed that the study area and surrounding counties included a dense network of lineaments, including through existing and proposed mines, mitigation banks and other conservation lands. Excavation, water use and operation of proposed mines associated with those fractures could result in irreversible adverse environmental impacts to those mitigation banks and other conservation lands linked by the network of fractures. To ensure sustainable developments and compliance with mitigation laws in this highly fractured area, adverse impacts to mitigation banks and other conservation lands from groundwater pumping and mining via fractures must be considered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".