Characterization of Excreted Salt from the Recretohalophytes <i>Distichlis spicata</i> and <i>Spartina pectinata</i>
Bibliographic record
Abstract
Recretohalophytes are salt‐tolerant plants that excrete excess salts through specialized glands on their leaf surfaces. They have the potential to be applied as a phytotechnology to remediate salt‐impacted soils by removing the salt from the soil and allowing it to be mobilized on the wind (haloconducted) and hence dispersed over a wide area where the salt ions will act as nutrients rather than pollutants. This study is the first to characterize the excreted salts of two recretohalophytic grasses, Spartina pectinata Bosc ex Link and Distichlis spicata (L.) Greene using scanning electron microscopy (SEM). At above‐optimal conditions for salt excretion (i.e., >65% humidity and >26°C), salt appeared on the stem and leaf surfaces as a sap‐like excretion that could not be easily mobilized. The mean diameter of the salt crystals excreted by S. pectinata (31 ± 24 μm) is significantly smaller than the diameter of those excreted by D. spicata (49 ± 22 μm) ( p < 0.05). Spartina pectinata excreted significantly more salt crystals per unit area of plant surface (60 ± 41 crystals mm −2 ) than D. spicata (27 ± 16 crystals mm −2 ). These salt crystal characteristics can now be used to assist with determining the optimal species for haloconduction, and in particulate dispersal modeling systems to help determine the fate of the excreted salts once they become airborne by wind. Core Ideas Recretohalophytes excrete salts that disperse via winds through “haloconduction.” The mean diameter of excreted salt crystals varied by plant species. The mean number of salt crystals per unit of leaf area varied by plant species. Air particulate modeling can use this to assess the effectiveness of salt dispersal. Salt excretion and dispersal may prove to be a useful form of salt remediation.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".