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Record W4310682253 · doi:10.1002/essoar.10512935.1

Limited Recharge of a Steady Deep Groundwater Aquifer in the Southern Highlands of Early Mars

2022· preprint· en· W4310682253 on OpenAlexaff
Eric Hiatt, Mohammad Afzal Shadab, S. P. S. Gulick, T. A. Goudge, Marc A. Hesse

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Aeronautics and Space Administration
KeywordsGroundwater rechargeAquiferDepression-focused rechargeGeologyHydraulic conductivityHydrology (agriculture)GroundwaterPrecipitationSurface runoffSoil scienceGeographySoil waterGeotechnical engineeringMeteorology

Abstract

fetched live from OpenAlex

To determine plausible groundwater recharge rates on early Mars, we develop analytic and numerical solutions for an unconfined steady-state aquifer beneath the southern highlands. We show that the aquifer’s mean hydraulic conductivity, $K$, is the primary constraint on the plausible magnitude of mean steady recharge, $r$. By restricting groundwater upwelling to Arabia Terra, using a mean hydraulic conductivity of, $K$ ${\sim}10^{-7}$ m/s, and varying shoreline elevations and recharge distributions, the mean recharge must be order of $10^{-2}$ mm/yr. Recharge for other values of $K$ can be estimated as $r$ ${\sim}10^{-5}\,K$. Our value is near the low end of previous recharge estimates and two orders-of-magnitude below the smallest precipitation estimates. This suggests that, for a steady hydrologic cycle, most precipitation forms runoff, not groundwater recharge. It is also plausible the transient aquifer response to recharge is sufficiently slow that no upwelling occurs prior to cessation of climatic excursions causing precipitation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.236
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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