MODELLING THE POTENTIAL DISTRIBUTIONS OF SAWO KECIK (MANILKARA KAUKI (L.)) DUBARD USING MAXENT TO SUPPORT CONSERVATIONS OF HISTORICAL AND CULTURAL VEGETATIONS IN DAERAH ISTIMEWA YOGYAKARTA PROVINCE
Bibliographic record
Abstract
Modelling the potential distributions of Sawo Kecik (Manilkara kauki (L.)) Dubard using MaxEnt to support conservations of historical and cultural vegeta-tions in Daerah Istimewa Yogyakarta Province. Reinwardtia 22(1): 55‒67. — Sawo kecik or Manilkara kauki (L.) Dubard, of the Sapotaceae family as it is formally known, is one of the species with significant cultural values in Yog-yakarta Province (DIY) culture because it symbolizes social righteousness. In connection with this, Yogyakarta's mu-nicipal and district governments have been encouraged to plant sawo kecik. Despite these efforts, there is still a lack of knowledge regarding the possible range of this species, and this knowledge is essential to promoting the conservation of M. kauki in DIY. With the help of isothermality, precipitation of driest month, precipitation seasonality, precipita-tion of driest quarter, and precipitation of warmest quarter data, this study tries to simulate the probable distributions of M. kauki throughout cities and districts in DIY. The model estimated 1,275 km2 of DIY areas was suitable for M. kauki that concentrated in the central parts, spanning from the west to the east of DIY. Yogyakarta City followed by Sleman District has the largest areas categorized from high to very high suitable for M. kauki. While, Gunung Kidul followed by Kulonprogo Districts have the largest areas categorized as low suitable. To conclude, M. kauki can adapt areas with moderate precipitation as low as 20 mm during driest month and as low as 100 mm during driest quarter. During warmest quarter, M. kauki requires precipitation with value of 700 mm. The conservation effort and M. kauki planting should then concentrate on Yogyakarta City and Sleman District since such locations are thought to have high appropriateness for the species.
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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".