Facies architecture in depositional systems resulting from the interaction of acidic springs, alkaline springs, and acidic lakes: case study of Lake Roto-a-Tamaheke, Rotorua, New Zealand
Bibliographic record
Abstract
The facies architecture in hot spring systems tends to become more varied and complicated as the degrees of freedom in the system increase. Discharge aprons fed by waters from a single vent will, for example, be characterized by predictable downslope facies changes that reflect downslope changes in water chemistry and temperature. The facies architecture, however, becomes exponentially more intricate when more factors start to impact the system. This phenomenon is readily apparent in the geothermal area around Lake Roto-a-Tamaheke (located in the Whakarewarewa Thermal Village, Rotorua, New Zealand) where the facies architecture developed in response to the interactions between acid lake, acid hot spring, and alkaline hot spring depositional regimes, with additional extraneous sediment being brought into the area by volcanic ash clouds, wind-blown pollen, and surface run-off from the surrounding drainage basin. Much of the complexity in the facies architecture of this system stems from the temporal variance in the lake level and the variable life cycles of the acid and alkaline hot springs. Fluctuations in lake level controlled the extent of lacustrine deposits, and flooding commonly quenched spring activity. During some periods various minerals precipitated around the acidic springs, whereas during other periods silica precipitated around the hot alkaline springs that are preferentially located along faults that transect the area. The interaction of all of these variables produces depositional regimes with largely unpredictable and highly variable facies architectures. As such, they contrast sharply with the more organized spring systems that develop when one type of water flows from a single vent.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".