Pendugaan Kesehatan Mangrove Rizhophora sp. Berdasarkan Analisis Kerapatan dan Morfometri Daun Di KEE Muara Kali Ijo, Kebumen
Bibliographic record
Abstract
ABSTRAK Kesehatan mangrove menggambarkan kondisi ekosistem mangrove yang berfungsi secara optimal dan mampu mendukung berbagai bentuk kehidupan di dalamnya. Namun, kondisi ini kerap terancam oleh aktivitas manusia serta perubahan lingkungan. Penelitian ini bertujuan untuk mengevaluasi kesehatan mangrove Rhizophora sp. melalui analisis keramatan dan morfometri daun. Penelitian dilakukan pada November 2024 di Kawasan Ekosistem Esensial (KEE) Muara Kali Ijo, Kebumen, Jawa Tengah. Fokus utama penelitian ini meliputi pengukuran kerapatan mangrove dan analisis Morfometri daunnya. Metode yang digunakan mencakup transek kuadran untuk mengetahui kerapatan serta penghitungan luas penampang daun Rhizophora sp. guna mengevaluasi kondisi Morfometrinya. Hasil menunjukkan bahwa stasiun 2 memiliki kerapatan tertinggi dengan nilai sebesar 4.500 ind/ha, sedangkan stasiun 3 paling rendah sebesar 3.200 ind/ha. Hasil analisis morfometri daun menunjukkan stasiun 1 memiliki persentase kerusakan daun rendah yang menggambarkan kondisi kesehatan mangrove terbaik, dan stasiun 3 memiliki persentase kerusakan tertinggi yang menunjukkan tingkat kesehatan terendah. Data ini menunjukkan bahwa kerapatan mangrove dan persentase kerusakan daun dapat digunakan sebagai data pendukung dalam penilaian kesehatan mangrove di suatu ekosistem. Kata Kunci: Kebumen, Kesehatan, Kerapatan, Mangrove, Morfometri
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".